D2.2.3
Version
Author
Dissemination
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2.4
URJC
CO
30/11/2016
Final
D2.3: State-of-the-art revision document v3
Project acronym:
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Keywords
NUBOMEDIA
NUBOMEDIA: an elastic Platform as a Service (PaaS) cloud
for interactive social multimedia
2014-02-01 to 2017-01-30
STREP
610576
http://www.nubomedia.eu
WP2
Victor Hidalgo
Report
Luis Lopez
11/2016
30/11/2016
State-of-the-art revision
The research leading to these results has been funded by the European Union’s Seventh
Framework Programme (FP7/2007-2013) under grant agreement nº 610576
FP7 ICT-2013.1.6. Connected and Social Media
D2.2.3: State-of-the-art revision document v3
DISCLAIMER
All intellectual property rights are owned by the NUBOMEDIA consortium members
and are protected by the applicable laws. Except where otherwise specified, all
document contents are: “© NUBOMEDIA project -All rights reserved”. Reproduction
is not authorized without prior written agreement. All NUBOMEDIA consortium
members have agreed to full publication of this document. The commercial use of any
information contained in this document may require a license from the owner of that
information.
All NUBOMEDIA consortium members are also committed to publish accurate and up
to date information and take the greatest care to do so. However, the NUBOMEDIA
consortium member scan not accept liability for any inaccuracies or omissions nor do
they accept liability for any direct, indirect, special, consequential or other losses or
damages of any kind arising out of the use of this information
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
2
D2.2.3: State-of-the-art revision document v3
Contributors:
URJC
LIVEU
VTOOLS
FRAUNHOFER
NAEVATEC
VTT
USV
ZED
TUB
TI
Internal Reviewer(s):
Constantin Filote (USV)
Noam Amram (LIVEU)
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
3
D2.2.3: State-of-the-art revision document v3
Version History
Version Date
01-04-2014
0.1
28-05-2014
24-09-2014
0.2
15-11-2014
0.3
26-12-2014
0.4
22-01-2015
1.0
Authors
Luis Lopez
Constantin Filote
Luis Lopez
Luis Lopez
Luis Lopez
Luis Lopez
1.1
21-04-2015
Luis Lopez
2.0
2.1
21-09-2015
20-01-2016
Luis Lopez
Luis Lopez
2.2
23-01-2016
Luis Lopez
2.3
23-08-2016
Luis Lopez
2.4
01-10-2016
Luis Lopez
Comments
Initial Version
First Reviewed
Additional topics added.
Additional topics added
Additional topics added
Added final contributions from
partners
Removed
all
previous
information
and
restarted
following review 1 outcome.
Crated new SotA structure
Integrated contributions from
partners.
Integrated
FRAUNHOFER
contribution
Integrated improvements in
SotA progresses by LIVEU
Integrated improvements in
SotA progresses by LIVEU
Added summary tables
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
4
D2.2.3: State-of-the-art revision document v3
Table of contents
1
Executive summary .......................................................................................................9
2
Introduction ....................................................................................................................9
3
Summary of NUBOMEDIA progresses and outcomes ..................................... 10
4
Cloud technologies for advanced media communications........................... 13
5
RTC media server technologies ............................................................................. 65
4.1
Cloud infrastructures for real-time media ..................................................................... 13
4.1.1 Description of current SoTA ....................................................................................... 13
4.1.2 NUBOMEDIA approach beyond SotA ......................................................................... 16
4.1.3 NUBOMEDIA outcomes.............................................................................................. 16
4.1.4 References .................................................................................................................. 17
4.2
Orchestration and Management of Real-Time Network Functions with guaranteed QoS
18
4.2.1 Description of current SoTA ....................................................................................... 19
4.2.2 NUBOMEDIA approach beyond SotA ......................................................................... 32
4.2.3 NUBOMEDIA outcomes.............................................................................................. 36
4.2.4 References .................................................................................................................. 37
4.3
PaaS for Real-Time Multimedia Applications ................................................................. 37
4.3.1 Description of current SoTA ....................................................................................... 43
4.3.2 NUBOMEDIA approach beyond SotA ......................................................................... 51
4.3.3 NUBOMEDIA outcomes.............................................................................................. 53
4.3.4 References .................................................................................................................. 53
4.4
Media monitoring in cloud infrastructures .................................................................... 54
4.4.1 Description of current SoTA ....................................................................................... 55
4.4.2 NUBOMEDIA approach beyond SotA ......................................................................... 59
4.4.3 NUBOMEDIA outcomes.............................................................................................. 60
4.4.4 References .................................................................................................................. 60
4.5
Deploying and installing media cloud infrastructures ................................................... 60
4.5.1 Description of current SoTA ....................................................................................... 61
4.5.2 NUBOMEDIA approach beyond SoTA ........................................................................ 64
4.5.3 NUBOMEDIA outcomes.............................................................................................. 64
4.5.4 References .................................................................................................................. 64
5.1
RTC media servers .......................................................................................................... 65
5.1.1 RTC media servers: an overview ................................................................................ 65
5.1.2 Description of current scientific and engineering SoTA ............................................. 69
5.1.3 NUBOMEDIA approach beyond SotA ......................................................................... 75
5.1.4 NUBOMEDIA outcomes.............................................................................................. 76
5.1.5 References .................................................................................................................. 78
5.2
Real-time Video Content Analysis on the cloud............................................................. 81
5.2.1 Description of current SoTA ....................................................................................... 83
5.2.2 NUBOMEDIA approach beyond SotA ......................................................................... 90
5.2.3 NUBOMEDIA outcomes.............................................................................................. 91
5.2.4 References .................................................................................................................. 92
5.3
Augmented Reality capabilities on real-time media servers ......................................... 93
5.3.1 Description of current SoTA ....................................................................................... 93
5.3.2 NUBOMEDIA approach beyond SotA ......................................................................... 97
5.3.3 NUBOMEDIA outcomes.............................................................................................. 99
5.3.4 References .................................................................................................................. 99
5.4
Interoperability on real-time media infrastructures servers ......................................... 99
5.4.2 NUBOMEDIA approach beyond SotA ....................................................................... 105
5.4.3 NUBOMEDIA outcomes............................................................................................ 106
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5.4.4 References ................................................................................................................ 106
5.5
Cloud APIs for accessing Media Servers....................................................................... 106
5.5.1 Related work ............................................................................................................ 108
5.5.2 NUBOMEDIA approach beyond SotA ....................................................................... 110
5.5.3 NUBOMEDIA outcomes............................................................................................ 113
5.5.4 References ................................................................................................................ 115
5.6
Real-time media APIs in smartphone platforms .......................................................... 117
5.6.1 Description of current SoTA ..................................................................................... 117
5.6.2 NUBOMEDIA approach beyond SotA ....................................................................... 120
5.6.3 NUBOMEDIA outcomes............................................................................................ 121
5.6.4 References ................................................................................................................ 122
5.7
Cloud Videoconferencing APIs ..................................................................................... 122
5.7.1 Description of current SotA ...................................................................................... 122
5.7.2 NUBOMEDIA approach beyond SotA ....................................................................... 125
5.7.3 NUBOMEDIA outcomes............................................................................................ 126
5.7.4 References ................................................................................................................ 126
5.8
Enhancing real-time media developer efficiency......................................................... 126
5.8.1 Description of current SoTA ..................................................................................... 127
5.8.2 NUBOMEDIA approach beyond SotA ....................................................................... 129
5.8.3 NUBOMEDIA outcomes............................................................................................ 129
5.8.4 References ................................................................................................................ 129
6
Real-time media in vertical segments .............................................................. 130
6.1
Real-time media for video surveillance and security ................................................... 130
6.1.1 Description of current SoTA ..................................................................................... 134
6.1.2 NUBOMEDIA approach beyond SotA ....................................................................... 140
6.1.3 NUBOMEDIA outcomes............................................................................................ 141
6.1.4 References ................................................................................................................ 141
6.2
Real-time media for news reporting ............................................................................ 142
6.2.1 Description of current SoTA ..................................................................................... 142
6.2.2 NUBOMEDIA approach beyond SotA ....................................................................... 144
6.2.3 NUBOMEDIA outcomes............................................................................................ 145
6.2.4 References ................................................................................................................ 146
6.3
Real-time media on e-health environments ................................................................ 146
6.3.1 Description of current SoTA ..................................................................................... 147
6.3.2 NUBOMEDIA approach beyond SotA ....................................................................... 153
6.3.3 NUBOMEDIA outcomes............................................................................................ 153
6.3.4 References ................................................................................................................ 153
6.4
Real-time media on games........................................................................................... 155
6.4.1 Description of current SoTA ..................................................................................... 156
6.4.2 NUBOMEDIA approach beyond SotA ....................................................................... 160
6.4.3 NUBOMEDIA outcomes............................................................................................ 160
6.4.4 References ................................................................................................................ 161
6.5
Real-time media for social TV ...................................................................................... 161
6.5.1 NUBOMEDIA approach beyond SotA ....................................................................... 162
6.5.2 NUBOMEDIA outcomes............................................................................................ 163
6.5.3 References ................................................................................................................ 163
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
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D2.2.3: State-of-the-art revision document v3
List of Figures
Figure 1 Feature comparison of IaaS frameworks [LASZEWSKI2012] - indicates a positive evaluation
(the more checkmarks the better). .................................................................................................................................... 14
Figure 2 Feature comparisons between OpenStack and other FOSS for IaaS .................................................. 14
Figure 3 A comparison chart between Xen, KVM, VirtualBox, and VMWare ESX [YOUNGE2011]. .......... 15
Figure 4 IBM's results for Docker vs. KVM results running Linpack on two sockets with 16 cores. Each
data point is the arithmetic mean obtained from ten runs. Error bars indicate the standard deviation
obtained overall runs. ............................................................................................................................................................. 15
Figure 5. ETSI NFV Architecture ......................................................................................................................................... 18
Figure 6. OpenMANO Architecture..................................................................................................................................... 19
Figure 7. High level overview of the Tacker focus on the NFV Architecture ..................................................... 21
Figure 8. Detailed architectural overview of the Tacker project ........................................................................... 22
Figure 9. Achitectural overview of Cloudify ................................................................................................................... 24
Figure 10. Mapping of Juju to NFV Architecture .......................................................................................................... 26
Figure 11. Service Manager (SM) internal architecture ........................................................................................... 27
Figure 12. Service Orchestrator (SO) internal architecture .................................................................................... 28
Figure 13. Cloud Controller (CC) internal architecture ............................................................................................. 28
Figure 14. High-level view of overall T-NOVA System Architecture ..................................................................... 30
Figure 15. T-NOVA Orchestrator platform, modules and itnerfaces .................................................................... 30
Figure 16. 5G Exchange architectural approach ......................................................................................................... 32
Figure 17. High-level Architecture of OpenBaton ........................................................................................................ 35
Figure 18. High-level Architecture of OpenBaton and the Generic VNFM inlcuding the EMS.................... 36
Figure 19 NIST PaaS Reference Architecture ................................................................................................................ 38
Figure 20 Gartner Refernece Archiecture for PaaS ..................................................................................................... 39
Figure 21 Forrester Research PaaS Reference Architecture ................................................................................... 40
Figure 22 Cloud Foundry Components Overview ......................................................................................................... 43
Figure 23 OpenShift Origin Architecture Overview..................................................................................................... 45
Figure 24Google App Engine High Level Overview ..................................................................................................... 47
Figure 25 Solutions Review report on 2016 Comparison Matrix Report ............................................................ 49
Figure 26 InfluxDB design ..................................................................................................................................................... 56
Figure 27. InfluxDB Web Interface for management ................................................................................................. 56
Figure 28. Graphite architecture ........................................................................................................................................ 57
Figure 29. How Graphite components interact ............................................................................................................. 58
Figure 30. Prometheus architecture ................................................................................................................................. 59
Figure 31. RTC applications, in general, and WebRTC applications, in particular, may use two different
models. As shown at the top, the peer-to-peer model is based on direct communication among clients.
This model provides minimum complexity and latency, but it also has important limitations. At the
bottom, the infrastructure-mediated model, where a media server is mediating among the
communicating clients. This model has higher latency and complexity, but it makes possible to enrich
RTC services with additional capabilities such as transcoding (i.e. interoperability), efficient group
communications (i.e. MCU or SFU models), recoding and media processing. ................................................... 66
Figure 32: Media capabilities provided by state-of-the-art media server include: transcoding (top),
group communications (middle) and archiving (bottom). ...................................................................................... 68
Figure 33: The most popular Computer Vision libraries ........................................................................................... 84
Figure 34: Microsoft Project Oxford .................................................................................................................................. 86
Figure 35: CloudCV Architecture ........................................................................................................................................ 88
Figure 36: CloudCV backend................................................................................................................................................. 89
Figure 37 IMS Layered Architecture ...............................................................................................................................101
Figure 38 WebRTC integration with IMS user agent and data repository ......................................................102
Figure 39 webRTC linking to IMS via NNI.....................................................................................................................102
Figure 11 OMA based IMS service architecture ..........................................................................................................103
Figure 41 Comparing SIP proxy with SIP-webRTC Gateway .................................................................................105
Figure 42 Review of VCF tools [GASPARINI2013] ......................................................................................................124
Figure 43: Video Surveillance industry segments ......................................................................................................130
Figure 44: Analog Video Surveillance System Architecture ...................................................................................132
Figure 45: IP Video Surveillance System Architecture .............................................................................................133
Figure 46: Analog vs IP cameras ......................................................................................................................................133
Figure 47. Video Surveillance as a Service (VSaaS) ..................................................................................................134
Figure 48: Next (left side) and Axis cameras (right side) .......................................................................................135
Figure 49: iSpy FOSS solution ............................................................................................................................................138
Figure 50: Framework for a Cloud-Based Multimedia Surveillance system ..................................................140
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
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D2.2.3: State-of-the-art revision document v3
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
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D2.2.3: State-of-the-art revision document v3
1 Executive summary
This document contains a revision on SotA (State-of-the-Art) on all technological areas
of interest of the project providing, for each of them, the following information:
• The current technological status including shortcomings and limitations.
• How the project is planning to advance them.
• The expected results including how these advance in the scientific and technical
domains.
2 Introduction
NUBOMEDIA a cloud platform specifically designed for hosting real-time interactive
multimedia services. For this, in this project, we have created a number of technological
enablers making possible for developers to use different PaaS APIs for creating media
applications leveraging advanced capabilities which include WebRTC and RTP
communications, media transcoding, media mixing, media routing and advanced media
processing with features such as Video Content Analysis and Augmented Reality.
As it can be inferred from this description, NUBOMEDIA is a quite ambitious project
that aims at evolving current SotA (State-of-the-Art) in many technological domains.
For the sake of simplicity, in this document we concentrate on the ones which have
more relevance for the project. Due to this, the document is organized in the following
sections:
• Cloud technologies for advanced media communications: this section is devoted
to analyzing SotA status on the area of cloud computing infrastructures and
orchestration: the ones having more relevance for NUBOMEDIA.
• Media server technologies: in this section we review current efforts for creating
Real-Time Communications media servers and their development APIs.
• Real-time media in vertical segments: This section comprises all the specific
vertical domains where NUBOMEDIA partners are involved. For every of them
we specify current trends in the market analyzing the features and limitations of
the most popular solutions.
For every of these sections, we specify the NUBOMEDIA approach for overcoming
current SotA as well as the expected outcomes generated by the project.
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
9
3 Summary of NUBOMEDIA progresses and outcomes
NUBOMEDIA progresses and outcome in the area of real-time media clouds
Technological area
NUBOMEDIA outcome
Scientific progresses
Cloud infrastructures for real- Fine tuning of an OpenStack/Docker None
time media
IaaS for deployment of RTC services
Innovation progresses
First worldwide FOSS IaaS for RTC.
Best practices for RTC IaaS
Orchestration and management
of real-time network functions
with guaranteed QoS
Extensions
to
OpenBaton
NFVO/VNFM adapted to RTC and
NUBOMEDIA needs.
Optimization of autoscaling
policies
for
multimedia
applications.
QoS
orchestration
of
networking resources.
First worldwide FOSS
adapted to RTC services
PaaS for Real-Time Multimedia
Applications
NUBOMEDIA PaaS Manager, API and
GUI
First worldwide FOSS PaaS specifically
adapted to RTC services
Media monitoring
infrastructures
NUBOMEDIA monitoring system
integrating KMS metrics with Graphite
NFV-based
architecture
description for an RTC PaaS
Novel AAA mechanisms for
CPaaS
Advanced RTC monitoring with
diagnose and testing purposes.
NUBOMEDIA autonomous installer
None
First worldwide FOSS installer for RTC
media PaaS
in
cloud
Deploying and installing media
cloud infrastructures
1
NFVO/VNFM
First worldwide FOSS monitoring system
for RTC services
Evidences and further information
Section 4.1
Conference publication 1 2
Post in very popular blog 3
Section 4.2
Conference publications 4
Publication in preparation
Section 4.3
Conference publications 5
Post in very popular blog 6
JCR journal paper 7
Section 4.4
Conference publications 8
Section 4.5
Spoiala, Cristian Constantin, et al. "Performance comparison of a webrtc server on docker versus virtual machine." Development and Application Systems (DAS), 2016 International Conference on.
IEEE, 2016.
2 Calinciuc, Alin, et al. "OpenStack and Docker: building a high-performance IaaS platform for interactive social media applications." Development and Application Systems (DAS), 2016 International
Conference on. IEEE, 2016.
3 https://webrtchacks.com/webrtc-media-servers-in-the-cloud/
4 Cheambe, Alice, et al. "Design and Implementation of a High Performant PaaS Platform for Creating Novel Real-Time Communication Paradigms." 19th International Innovation in Clouds, Internet and
Networks (ICIN) Conference. 2016.
5 Boni García, Micael Gallego, Luis López, Giuseppe Antonio Carella, Alice Cheambe. "NUBOMEDIA: an Elastic PaaS Enabling the Convergence of Real-Time and Big Data Multimedia". The IEEE
International Conference on Smart Cloud (SmartCloud 2016). November 18th-20th, 2016, New York, USA.
6 https://bloggeek.me/nubomedia-webrtc-paas/
7 Lopez-Fernandez, L., Gallego, M., Garcia, B., Fernandez-Lopez, D., & Lopez, F. J. (2014). Authentication, authorization, and accounting in WebRTC PaaS infrastructures: The case of Kurento. IEEE
Internet Computing, 18(6), 34-40.
8 Boni García, Luis López-Fernández, Francisco Gortázar, and Micael Gallego. Analysis of video quality and end-to-end latency in WebRTC. In Fifth IEEE International Workshop on Quality of
Experience for Multimedia Communications (QoEMC2016) IEEE GLOBECOM 2016, Washington DC, USA, December 2016.
D2.2.3: State-of-the-art revision document v3
NUBOMEDIA progresses and outcome in the area of real-time media server technologies
Technological area
NUBOMEDIA outcome
Scientific progresses
RTC media servers
Kurento WebRTC media server Novel modular architecture for
capabilities
RTC media servers.
WebRTC media server testing
Real-time VCA in the Cloud
Diverse Kurento VCA modules
None
AR capabilities on RTC
Diverse Kurento AR modules
None
Interoperability on RTC media
servers
Cloud APIs for accessing media
servers
Kurento agnostic bin
RTC APIs on smartphone
platforms
Cloud videoconferencing APIs
NUBOMEDIA Android and iOS SDKs
Novel
mechanism
for
transparent transcoding
Creation, specification and
validation of a novel modular
cloud API for RTC.
None
Kurento Room and Tree APIs
None
Enhancing
efficiency
NUBOMEDIA
GUI
None
RTC
developer
Kurento Media Server API
visual
development
Innovation progresses
First multi-purpose and modular FOSS
WebRTC media server
First cloud VCA capabilities interoperable
with WebRTC enabling interoperable
WWW AR in both desktop and mobile
First cloud AR capabilities interoperable
with WebRTC
First FOSS implementation of a
transparent transcoding technology
First FOSS implementation of a modular
RTC media server API
First FOSS multi-layered RTC smartphone
APIs
First
FOSS
specification
and
implementation of a Tree (i.e broadcasting)
API
First visual GUI for agile learning of RTC
development
Evidences and further information
Section 5.1
Conference publications 9, 10, 11
Journal paper (in review) 12, 13
Post in popular blog 14
Section 5.2
Section 5.3
Section 5.4
Conference publication 15
Section 5.5
JCR journal paper 16
Section 5.6
Section 5.7
Section 5.8
9
Fernández, Luis López, et al. "Kurento: a media server technology for convergent WWW/mobile real-time multimedia communications supporting WebRTC." World of Wireless, Mobile and
Multimedia Networks (WoWMoM), 2013 IEEE 14th International Symposium and Workshops on a. IEEE, 2013.
10 Boni García, Luis López-Fernández, Micael Gallego, and Francisco Gortázar. Testing Framework for WebRTC Services. In 9th EAI International Conference on Mobile Multimedia Communications
(MOBIMEDIA), pages 40–47, Xi’an, China, June 2016.
11 Luis López, Miguel París, Santiago Carot, Boni García, Micael Gallego, Francisco Gortázar, Raul Benítez, Jose A. Santos, David Fernández, Radu Tom Vlad, Iván Gracia, and Francisco Javier López.
Kurento: The WebRTC Modular Media Server. In Proceedings of the 2016 ACM on Multimedia Conference, pages 1187–1191, Amsterdam, The Netherlands, October 2016.
12 Boni García, Francisco Gortázar, Luis López-Fernández, Micael Gallego, Miguel París. WebRTC Testing: Challenges and Practical Solutions. IEEE Communications Standards Magazine. June
2017/Real Time Communications in the Web. Under review (paper id COMSTD-17-00005).
13 Boni García, Luis López-Fernández, Micael Gallego, Francisco Gortázar. Kurento: the Swiss Army Knife of WebRTC Media Servers. IEEE Communications Standards Magazine. June 2017/Real
Time Communications in the Web. Under review (paper id COMSTD-17-00006).
14 https://webrtchacks.com/kurento/
15 Fernandez, L. L., Diaz, M. P., Mejias, R. B., Lopez, F. J., & Santos, J. A. (2013, October). Catalysing the success of WebRTC for the provision of advanced multimedia real-time communication
services. In Intelligence in Next Generation Networks (ICIN), 2013 17th International Conference on (pp. 23-30). IEEE.
16 López-Fernández, Luis, et al. "Designing and evaluating the usability of an API for real-time multimedia services in the Internet." Multimedia Tools and Applications (2016): 1-58.
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
11
D2.2.3: State-of-the-art revision document v3
NUBOMEDIA progresses and outcome in the area of vertical segments
Technological area
NUBOMEDIA outcome
Scientific progresses
RTC for video surveillance
and security
Security demonstrator
None
RTC for news reporting
TV broadcasting demonstrator
None
RTC on e-health
Health communicator demonstrator
None
RTC on games
Social game demonstrator
None
RTC for social TV
Social TV demonstrator
None
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
Innovation progresses
First application combining WebRTC
and archiving for video surveillance
(i.e. seek)
First application/service combining
news gathering using WebRTC
connected to Broadcasters systems
First application combining WebRTC
and multi-sensory AR.
Enhanced security and traceability for
professional RTC services.
First games leveraging WebRTC
media and cloud AR
First application combining WebRTC
and cloud VCA for smart-home
environments
Evidences
information
Section 6.1
and
further
Section 6.2
Section 6.3
Section 6.4
Section 6.5
12
4 Cloud technologies for advanced media communications
4.1 Cloud infrastructures for real-time media
4.1.1 Description of current SoTA
Regarding IaaS cloud infrastructures, in current state-of-the-art we can find different
solutions.
Commercial IaaS solutions for - virtual infrastructure
• VMware vSphere - https://www.vmware.com/products/vsphere
o Is a commercial solution from VMware for cloud computing and is
targeted mostly to enterprise companies.
• Microsoft
System
Center
http://www.microsoft.com/en-us/servercloud/products/system-center-2012-r2/
• Oracle Cloud IaaS - https://www.oracle.com/cloud/iaas.html
These solutions have a number of limitations:
• They are expensive in economic terms.
• Limited capability in integrating with third party solutions
• Closed source, meaning you can not modify the platform to fulfill any special
needs
FOSS (free open source solutions):
• OpenNebula
• Apache CloudStack
• OpenStack
• Eucalyptus 2.0
• Nimbus
In this case, the limitations include:
• Lack of suitable paid / professional support for some of them.
For NUBOMEDIA objectives, FOSS solutions are more relevant. The following tables
show a comparison among them.
D2.2.3: State-of-the-art revision document v3
Figure 1 Feature comparison of IaaS frameworks [LASZEWSKI2012] - indicates a positive evaluation (the more
checkmarks the better).
Figure 2 Feature comparisons between OpenStack and other FOSS for IaaS
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
14
D2.2.3: State-of-the-art revision document v3
The NUBOMEDIA project is particularly interested in OpenStack, which is used as
IaaS in the project. The following virtualization solutions are available for OpenStack:
Xen, KVM and VMware ESXi.
Paravirtualization
Full
virtualization
Host CPU
Guest CPU
Host OS
Guest OS
VT-x / AMD-v
Cores
supported
Memory
supported
3D Acceleration
Live Migration
License
Xen
Yes
KVM
No
VirtualBox
No
VMWare
No
Yes
Yes
Yes
Yes
x86, x86-64,
IA-64
x86, x86-64,
IA-64
Linux, UNIX
x86, x86-64,
IA-64, PPC
x86, x86-64,
IA-64, PPC
Linux
x86, x86-64
x86, x86-64
x86, x86-64
x86, x86-64
Linux,
Windows,
UNIX
Optional
128
Linux,
Windows,
UNIX
Required
16
Windows,
Linux, UNIX
Linux,
Windows,
UNIX
Optional
32
Proprietary
UNIX
Linux,
Windows,
UNIX
Optional
8
4TB
4TB
16GB
64GB
Xen-GL
VMGL
Open-GL
Yes
GPL
Yes
GPL
Yes
GPL/Proprietary
Open-GL,
DirectX
Yes
Proprietary
Figure 3 A comparison chart between Xen, KVM, VirtualBox, and VMWare ESX [YOUNGE2011].
One of the features that NUBOMEDIA requires is to have the instance boot time as
small as possible in order to allow deployment of new media servers on demand
whenever the number of users accessing applications increases rapidly. For all the
previously enumerated solutions the boot time is somewhere between 2 and 4 minutes
depending on the distribution type. However, recently a new technology has emerged
and it is gaining more and more adopters: container. In particular, we are interested in
Docker containers. Many research works demonstrate that Docker performs better than
hypervisors such as KVM [IBM Research paper].
Figure 4 IBM's results for Docker vs. KVM results running Linpack on two sockets with 16 cores. Each data point is
the arithmetic mean obtained from ten runs. Error bars indicate the standard deviation obtained overall runs.
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The Docker platform has announced the coming of new offerings: Docker Swarm
[DOCKER/SWARM] and Docker Compose [DOCKER/COMPOSE]. When they are
combined together, they may provide a solution to the service discovery problem.
Docker Compose already provides a very limited service discovery mechanism
[STUBBS], but it currently only works on a single host and it does not update as
containers are stopped and restarted. It is unclear exactly what Docker Swarm will
enable as details of that feature have yet to be announced.
Another platform that is using Docker technology is Agrave. Agave is a Science-as-aService platform, an ecosystem of open-source hosted developer APIs and tools for
building science gateways [AGAVEAPI], [DOOLEY]. Agave is the backbone of NSF
funded projects: NSF Plant Cyber-Infrastructure Program (DBI-0735191), NSF Plant
Genome Research Program (IOS-1237931 and IOS-1237931), NSF Division of
Biological Infrastructure (DBI-1262414), NSF Division of Advanced CyberInfrastructure (1127210), and the National Institute of Allergy and Infectious Diseases
(1R01A1097403) such as the iPlant collaborative with over 15.000 users submitting
thousands of jobs and moving over two petabytes of data every month. Production
components of Agave run as Docker containers packaged up with Serfnode for service
discovery. A Serfnode is a Docker image containing a serf agent and a supervisor
instance [STUBBS]. Additionally, iPlant has open sourced the Agave deployer
program, itself a Docker container, that interested parties can use to stand up their own
Agave instance—nearly 40 containers in total - across multiple virtual machines with a
single command. Users can bring their own apps as source code, binary code, VM
images, or Docker images and mix and match them with apps from the catalog to create
their own boutique pipelines they can save, reuse, and share. Because Agave manages
the end-to-end lifecycle of an application's execution, users are able to get out of the
sysadmin business and think of their science as a service. The only configurations
needed for the Agave deployer are the IP addresses.
4.1.2 NUBOMEDIA approach beyond SotA
Given the SotA status described above, the NUBOMEDIA progresses beyond SotA are
more pragmatic than fundamental. Our main contribution was to enable Docker to
work seamlessly inside OpenStack. In order to achieve this there are many alternatives,
but only one making possible for virtual instances to run on Docker as a hypervisor:
nova-docker.
Nova-docker is an alpha version hypervisor driver for OpenStack Nova Compute. It was
first introduced within the Havana release, being ported up to the latest stable release.
We have evolved nova-docker adding a patch to automatically support Docker images
provisioning on all compute nodes running Docker as a hypervisor on OpenStack.
4.1.3 NUBOMEDIA outcomes
We enabled the possibility to have docker instances on top of the OpenStack compute
nodes by implementing the nova-docker driver. A lot of missing documentation on the
OpenStack page and the wiki page for nova-docker [nova-docker wiki] made the
deployment not straightforward, but we documented all the needed steps enabling an
easy and fast deployment of the Docker hypervisors by other IaaS. The instructions on
how to properly deploy and configure nova-docker can be found on the WP6
deliverable.
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We found a missing features for the nova-docker driver. The provisioning of container
images that are added by users in the Glance image repository. Without this feature we
couldn’t have used KMS Docker containers on NUBOMEDIA because the IaaS was not
able to start them on the compute nodes. In order to solve this problem we created a
patch for the nova-docker which is written in python and uses the OpenStack APIs. The
patch is available on a public github repository [nova-docker patch].
These outcomes shall be disseminated in the following way. First, we plan to make a
pull request to the [nova-docker repository] and contribute it to the community to enable
this capability directly in the hypervisor. Second, using the knowledge created on IaaS
part, we are planning to publish a paper at the International Conference on Development
and Application Systems 2016 [DAS conference 2016].
4.1.4 References
Websites
[nova-docker patch] https://github.com/usv-public/nubomedia-nova-docker
[nova-docker wiki] https://wiki.openstack.org/wiki/Docker ;
[nova-docker repository] https://github.com/openstack/nova-docker ;
[DAS conference 2016] http://www.dasconference.ro/ ;
[DOCKER/SWARM] https://github.com/docker/swarm/ ;
[DOCKER/COMPOSE] https://github.com/docker/compose/ ;
[AGAVEAPI] http://agaveapi.co/ ;
[CALINCIUC2013] Calinciuc, Alin, “OpenStack, the right solution for private cloud”,
2013, http://academia.edu/10599168/OpenStack_the_right_solution_for_private_cloud ;
[CALINCIUC2014] Calinciuc, Alin; Spoiala, Cristian, “Docker - container
virtualization and its impact in NUBOMEDIA”, 2014,
http://academia.edu/10599188/Docker__container_virtualization_and_its_impact_in_NUBOMEDIA .
Papers and books
[IBM Research paper]
http://domino.research.ibm.com/library/cyberdig.nsf/papers/0929052195DD819C85257
D2300681E7B/$File/rc25482.pdf ;
[STUBBS2015] Stubbs, Joe; Moreira, Walter; Dooley, Rion, “Distributed Systems of
Microservices Using Docker and Serfnode”, 7th International Workshop on Science
Gateways, (2015): 34-39.
[DOOLEY2012] Dooley, R.; Vaughn, M.; Stanzione, D.; Terry, S., and Skidmore, E.
“Software-as-a-Service: The iPlant foundation API” 5th IEEE Workshop on Many-Task
Computing
on
Grids
and
Supercomputers,
(2012),
http://datasys.cs.iit.edu/events/MTAGS12/p07.pdf .
[ANDERSON2015] Anderson, Charles, “Docker”, IEEE Software, MAY/JUNE,
(2015): 102-105.
[LASZEWSKI2012] Laszewski, G. von; Diaz, J.; Wang, F. and Fox, G.
C.,“Comparison of MultipleCloud Frameworks,” IEEE Cloud 2012, Honolulu, HI, June
2012, (2012).
[YOUNGE2011] Younge, Andrew J.; Henschel, Robert; Brown, James T.; Laszewski,
Gregor von; Qiu, Judy; Fox, Geoffrey C., “Analysis of Virtualization Technologies for
High Performance Computing Environments”, IEEE 4th International Conference on
Cloud Computing CLOUD '11, (2011): 9-16.
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4.2 Orchestration and Management of Real-Time Network Functions with
guaranteed QoS
Network Function Virtualization (NFV) [ETSI_WP] considers the implementation of
NFs as entities only in software that run over the Network Function Virtualization
Infrastructure. Considering that Media Servers are intrinsically Network Functions,
NUBOMEDIA followed the ETSI NFV specification for the Management and
Orchestration (MANO) [MANO] of the media service components.
In Figure 5, it is shown the ETSI NFV architecture as described in the MANO
specification. Our main focus in on the management and orchestration component
highlighted by the red box. The Network Function Virtualization Orchestrator (NFVO)
is the component managing the lifecycle of a Network Service (NS) composed by
multiple MS and a Cloud Repository, while one or more Virtual Network Function
Managers (VNFM) are in charge of the lifecycle of the VNF (refer to D2.4.2 and D3.2
for more details about the NUBOMEDIA Architecture and software implementation).
Figure 5. ETSI NFV Architecture
In order to properly identify the software components which can be used for
implementing this functional elements, it is important to firstly identify the main
functional requirements this tool should support.
This component must support:
• On-demand provisioning of Network Services as combination of multiple
Network Functions.
• Composition of multiple Network Functions in a Network Service providing
different capabilities.
• Broad Network Access so that those Network Services could be accessed from
different locations.
• Monitoring of all the resources used by the different Network Functions.
• Mechanisms for providing network slicing, specifically providing different QoS
requirements to multiple Network Services deployed on the NFV Infrastructure
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•
•
•
Autoscaling of VNFs.
On demand scaling of VNFs.
OpenStack integration.
4.2.1 Description of current SoTA
In this section are presented the current on-going activities on management and
orchestration in cloud environment.
4.2.1.1 OpenMANO
OpenMANO [OPEN_MANO] represents an open source implementation of the ETSI
NFV architecture. Figure 6 shows the mapping between the OpenMANO components
and the ETSI NFV Architecture.
Figure 6. OpenMANO Architecture
OpenMANO follows an NFVO-centric approach what means that the NFVO is mainly
responsible for interacting with the VIM by creating, deleting and managing instances,
images, flavors and networks/links whereas the VNF lifecycle management is split
between NFVO (VNF instantiation and termination) and VNFM. Apart from that, the
VNFM is also in charge of VNF performance monitoring, fault management and event
management.
Basically, OpenMANO consists of three main components:
• openmano: This component includes the reference implementation of an
NFVO. It communicates with the VIM through its REST API and is in
charge of managing NFV services by being responsible for the creation and
deletion of VNF templates, VNF instances, network service templates and
network service instances by interacting with the openvim. The openmano
provides a northbound API for managing these service entities respectively.
• openmano-gui: The GUI, accessible via a web dashboard, interacts with
openmano through its API to administrate termplates, services and instances.
• openvim: The openvim offers enhanced cloud services to openmano through
a REST API including the creation, deletion and management of images,
flavors, instances and networks in the NFVInfrastructure. The openvim
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interacts with an openflow controller for providing those network
capabilities.
Since OpenMANO is a relatively new project, started in 2015, it is still under
development. The first stable version (v0.3) for normal use was released in July 2015
supporting basic functionalities like deploying and terminating simple Network
Services - called Network Scenarios in their terminology. This version was restricted to
the usage of hypervisors and compute nodes controlled via libvirt whereas the
Openflow switches were controlled by proactive rules (using floodlight). This brings
complexity to the configuration and limitations as well:
• Hosts must be managed
• No identity management
• No concept of subnets
• Image uploading must be done manually by the end user
• Hardware open flow switch required for building a IaaS
The latest version (v0.4) was released at the end of 2015 supporting an additional
Openflow controller, namely opendaylight. Additionally, it was integrated a Multi-VIM
support for supporting Openstack as well. A first VNFM (GenericVNFM)
implementation is already mentioned but still under development at this point in time.
Therefore, it cannot support complex VNF management (complex lifecycle
management and autoscaling), performance monitoring, fault management and event
management.
4.2.1.2 Tacker
Tacker, launched in 2014, is an open source implemented OpenStack project building a
Network Function Virtualization Orchestrator (NFVO) including an in-built general
purpose Virtual Network Function Manager (VNFM) allowing the deployment and
management of Virtual Network Functions (VNFs). Tacker is directly integrated into
OpenStack and hence, it provides only this type of cloud environment. As mentioned
before and shown in Figure 3, Tacker focuses on the reference implementation of an
NFVO and a VNFM aligned to ETSI’s NFV MANO specifications. This project is
highly active with almost daily implementation activities and furthermore, it is
documented using Blueprints with weekly-scheduled meetings summarized and
published frequently on their website.
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Figure 7. High level overview of the Tacker focus on the NFV Architecture
Figure 8 depicts a more detailed overview of Tacker’s architecture, its components and
relations between them. First of all, Tacker combines the NFVO and VNFM in a single
component whereas, internally, the functionalities are clearly distinct as described
below.
The NFVO is responsible for the high-level management of VNFs. Therefore, it makes
use of a template-based end-to-end Network Service deployments able to compose
several VNFs to a more complex NS. Furthermore, the NFVO is in charge of managing
resources in the VIM – Vim Resource Checks and Resource Allocation. It allows to
orchestrate VNFs across multiple VIMs using VNF placement policies to ensure
efficient placement of VNFs. VNFs itself are connected through Service Function
Chaining (SFC) that are described in the VNF Forwarding Graph Descriptor
(VNFFGD).
The VNFM, in turn, is in charge of managing groups of components that belongs to the
same VNF instance controlling the basic lifecycle of a VNF (define, start, stop,
undefined) and therefore, it facilitates the initial configuration. Additionally, it is also
responsible for performance and health monitoring of already deployed VNFs. The
VNFM uses these performance and health information for processing auto-healing and
autoscaling where monitored parameters and corresponding actions are defined in
policies. The Tacker VNF Catalogue, also maintained by the VNFM is basically the
Repository of VNF Descriptors stored in the Tacker DB. Therefore, it provides an API
to on-board, update and delete VNFDs. VNFDs can be defined additionally in TOSCA
templates for describing VNF attributes like Glance image IDs, Nova properties
(Placement, CPU Pinning, Numa policies, etc), Performance Monitoring policies, AutoHealing policies and Auto-Scaling policies.
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Figure 8. Detailed architectural overview of the Tacker project
Tacker provides several frameworks to facilitate configuration and management. An
extendable Management Driver Framework is supported for VNF Auto Configuration
that simplifies the VNF configuration based on Service selection. Injecting initial
configurations can either be done through config-drive (special configuration drive that
attaches to the instance when it boots) or custom management driver (connecting via ssh
/ REST API and apply configuration) also in active state. Another framework is the
extendable Vendor and Service specific Health Monitoring Driver framework enabling
Tacker to do health checks and healing (e.g. auto-restart on failure) once the VNF
becomes ready. A similar framework is the extendable Vendor and Service specific
Performance Monitoring Driver framework used for VNF autoscaling. Continuous
performance monitoring according to KPIs described in the VNFD are used to
processing VNF based autoscaling based on policies as well.
Since Tacker is a relatively new project where the implementation is at a very early
stage, not all of the features mentioned are already supported. The following list shows
next steps and the roadmap announced by the developers of Tacker:
• TOSCA NFV Profile support (using heat-translator)
• MANO API enhancements
• Enhanced Health Monitoring (framework, http-alive, etc)
• Auto Scaling support
• Support for NSD and VNFFG
• VNFFG to SFC mapping
Tacker uses Heat as the resource orchestration engine. The OpenStack Heat
Orchestrator is completely integrated with OpenStack and is an utility able to manage
multiple composite cloud applications using templates, over both an OpenStack-native
ReST API and a CloudFormation-compatible Query API.
A Heat Orchestration Template (HOT) is sent to Heat in order to deploy a specific
topology. The assignment of the Heat template is to define a topology infrastructure for
a Cloud application, using a readable and writable way of representation. Heat
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templates typically take the shape of plain Yaml documents. Inside the HOT are also
defined all the policies needed to enable the auto scaling. In particular, can be set
policies regarding CPU and memory of each VM (Media Server in our specific case).
There are also relationships between resources thus to infer a particular launch order on
OpenStak. Those ones are likewise represented in the templates and Heat will follow
the correct launching order.
The HOT template provides different fields:
• heat_template_version
o This key indicates that the YAML document is a HOT template of the
specified version.
• description
o This optional key allows for giving a description of the template, or
theworkload that can be deployed using the template.
• parameter_groups
o This section allows for specifying how the input parameters should be
grouped and the order to provide the parameters in. This section is
optional and can be omitted when necessary.
• Parameters
o This section allows for specifying input parameters that have to be
provided when instantiating the template. The section is optional and can
be omitted when no input is required.
• Resources
o This section contains the declaration of the single resources of the
template. This section with at least one resource should be defined in any
HOT template, or the template would not really do anything when being
instantiated.
In the resources field it is possible to define an auto scaling group that can scale
arbitrary resources. The auto scaling system of Heat follows the concepts used by AWS
4.2.1.3 Cloudify
Cloudify [CLOUDIFY] is an enterprise-class open source Platform as a Service (PaaS)
stack providing the full end-to-end lifecycle of NFV orchestration through a simple
TOSCA-based YAML blueprint following a topology-driven and application-centric
approach. As claimed by themselves: “Cloudify is the only pure-play orchestration
framework uniquely positioned to fit into heterogeneous enterprise and Telco
environments”. Therefore, many big Telcos have chosen Cloudify in the sense of being
open source and modular in nature. Thanks to the “pluggability” it allows to make use
of a various set of toolsets and environments including many plugins to interface, for
instance, with peripheral network databases. Furthermore, it supports multiple clouds,
data centers and availability zones.
The architecture of Cloudify, as depicted in Figure 9, consists of three main
components:
• Manager (Orchestrator): The manager is a stateful orchestrator that deploys and
manages applications (described in blueprints). The manager is in charge of
running automation processes described in workflow scripts and forwards
execution commands to the agents.
• Agents: Agents are responsible for executing commands based on manager’s
requests. In general, there is one agent per application deployment and
optionally, an agent on each application VM. The manager side agents manage
IaaS related tasks, like create a VM or network, assigning floating IPs, and
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•
allowing to execute tasks remotely via REST for example. As mentioned before,
applications side agents are optionally and located on the application VM to
install plugins and execute tasks locally.
CLI client: The CLI client provides two main functions. It allows Manager
Bootstraping for installing the manager with the preferred tool. Furthermore, it
allows Managing Applications in order to simplify the deployment and
management of applications including log and event browsing.
Figure 9. Achitectural overview of Cloudify
Cloudify lays between the application and the chosen CPI. Thanks to it, an application
is able to focus on doing what it does best, it will be the task of Cloudify to manage the
resources it needs and to make sure that they are available independently of which cloud
and stack will be employed. Thanks to its design, Cloudify is able to offer some
features, for instance, without any changes to the code, it is possible to move your
application to the cloud, without any concerns about the application stack, database
store or any other middleware components it uses. Therefore, no code has to be changed
if you want to transfer your application to the Cloud. Moreover, Cloudify supports
public clouds (Amazon EC2, Windows Azure, Rackspace, etc.) and private clouds
(OpenStack, CloudStack, VMWare vCloud, Citrix XenServer, etc.)
The application is completely isolated from the underlying CPI to support enterprises
that want to deploy the same application in multiple environments (for cloud bursting).
All the lifecycle of the application is managed by Cloudify's mechanism. Cloudify
makes use of recipes in order to define an application, its services and their
interdependencies, how to monitor, self-heal, scale them and their resources. So the
process to deploy and manage an application results from:
1.
Preparing the deployment
a.
Set up the cloud and describe your machines in the cloud driver
b.
Prepare the binaries required for your services
c.
Describe the application lifecycle and its services in recipes
2.
Deploying the services and application
a.
Provisions machines in the cloud using cloud drivers
b.
Downloads, installs, and configures services
c.
Installs your application
d.
Configures the monitoring and scaling features
3.
Monitoring and managing the deployment using the Cloudify web
management console or the Cloudify shell
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The following list of benefits can be seen as a summarization of already mentioned
features with additional aspects:
• TOSCA-based, pure-play standard-driven orchestration
• Topology-driven and application-centric management and monitoring of the
entire NFV lifecycle
• Integration with any tool chain
• Support for any application stack, with native OpenStack, bare metal, and virtual
appliance support (enabling portability to any cloud and hybrid cloud models)
• Support for containerized and non-containerized workloads
• Designed for federated deployment
• Support legacy network functions
• Built in auto-healing and auto-scaling policies for deployed applications
• Embeddable (OEM)
4.2.1.4 Juju
Juju [JUJU] is an open source project based on a universal service modelling system
with a service oriented architecture and service oriented deployments. It allows to
deploy, configure, manage, maintain and scale cloud services easily and efficiently on
public clouds, physical servers, OpenStack and containers. The basic approach of Juju is
to make use of so-called Charms that contain all the instructions necessary for
deploying and configuring cloud-based services. These Charms can be composed to a
more complex service – also known as Service Composition. Service Composition is
one of the most important features of Juju. In particular, it allows the combination of
multiple services into a single functional system by providing two mechanisms for such
composition:
• Integration of services across network through standard interfaces
• Co-locate services on a common machine
Originally, Juju was not intended to follow the ETSI NFV MANO specifications but the
impact of NFV solutions in the growing market convinced to try at least a mapping of
Juju to the ETSI NFV architecture ideas. Actually, as claimed by the developers of Juju,
the service-orientation makes Juju well suited to the role of the VNFM. From this point
of view, Juju enables higher-level orchestrators to make decisions and communicate
them clearly and simply to the underlying model provided by Juju.
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Figure 10. Mapping of Juju to NFV Architecture
Again, as depicted in Figure 6, Juju can be seen as a generic Virtual Network Function
Manager (generic VNFM) in the ETSI NFV architecture. It takes care of managing the
service lifecycle by using hooks implemented inside Charms (language-aware).
Basically, these hooks are scripts and will be executed at the specific point in time of
the lifecycle, namely when installing, configuring, starting, upgrading or stopping the
service. Also dependencies can be controlled individually by using relation hooks
declared by the corresponding Charm. These relationships between Charms are
modelled by Juju to provide a topological view of service dependencies (control flows)
whereas the data plane is not influenced by Juju (provides only information to support
acceleration of the data plane) because it is mainly focused on controlling and
coordinating semantics. For this reason, Juju provides also the placement of service
units on machines based on a constraint language.
Specifically, in terms of ETSI NFV specifications, Juju supports both Vi-Vnfm and OrVi based resource orchestration by passing information to the underlying substrate
(defined by the constraint language or even by an external agent) to let Juju place the
units explicitly on predefined resources.
Bringing all together: Juju provides a multi-cloud and multi-datacenter infrastructure to
deploy and manage complex VNFs including VNFM and VNFO functions. One of the
most innovative approaches of Juju is to decouple the logic of managing services from
the orchestration frameworks itself. Nevertheless, it does not follow the ETSI NFV
information model and does not provide any ETSI NFV compliant interfaces as well.
This makes it very hard to extend current functionalities like introducing custom
autoscaling or network QoS management.
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4.2.1.5 Hurtle and the Mobile Cloud Networking approach
Hurtle [HURTLE] is an orchestration framework developed by the ICCLab in the
context of the project Mobile Cloud Networking (MCN) part of the FP7 program.
Hurtle supports service composition of different services.
Hurtle provides three main components:
• Service Manager (SM) the endpoint to the Enterprise End User (EEU) providing
an API for instantiating new tenant services
• Service Orchestrator (SO) managing the lifecycle of a tenant service instance
• Cloud Controller (CC) middleware providing an abstracted API on the
infrastructure layer
The SM, shown in Figure 11, is composed by different functional elements. The CLI
and UI interact with the Northbound Interface (NBI) and expose graphical and
command line interfaces for allowing the EEU to instantiate service instances. The main
role of the SM is to manage the lifecycle of the SO. Indeed, in the MCN terminology,
an instance of the SO is instantiated per service instance. Therefore, the SM contains a
SO registry (where all the SO bundles are present) and interacts with the CC for
deploying the SO on top of the PaaS system.
Figure 11. Service Manager (SM) internal architecture
The SO is the component taking care of the lifecycle of services. In particular, hurtle
gives only some guidelines on how the SO should be implemented, providing only a
general purpose SO based on the Heat IaaS orchestrator. The idea is to describe the
services in different template graphs, one describing infrastructure resources
(Infrastructure Template Graph) and one describing the service instance components
(Service Template Graph). The important thing is that the Northbound Interface of the
SO exposes an OCCI interface for the execution of the lifecycle steps described before.
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Figure 12. Service Orchestrator (SO) internal architecture
The CC provides a layer of abstraction of the Infrastructure. It provides abstracted API
for allowing the SO to deploy the Infrastructure Template Graph on a large scale
distributed infrastructure. Additionally it provides an API to the SM for generating the
containers on top of which the SO will be executed.
Figure 13. Cloud Controller (CC) internal architecture
Hurtle divides the lifecycle of a service in 6 key phases [HURTLE]:
• Design: where the topology and dependencies of each service component is
specified. The model here typically takes the form of a graph.
• Implementation: This is where the developer(s) needs to implement the actual
software that will be provided as a service through hurtle
• Deploy: the complete fleet of resources and services are deployed according to a
plan executed by hurtle. At this stage they are not configured.
• Provision: each resource and service is correctly provisioned and configured by
hurtle. This must be done such that one service or resource is not without a
required operational dependency (e.g. a php application without its database).
• Runtime: once all components of an hurtle orchestration are running, the next
key element is that they are managed. To manage means at the most basic level
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•
to monitor the components. Based on metrics extracted, performance indicators
can be formulated using logic-based rules. These when notified where an
indicator’s threshold is breached, an Orchestrator could take a remedial action
ensuring reliability.
Disposal: Where a hurtle service instance is destroyed.
The following list shows next steps and the roadmap announced by the developers of
hurtle:
• Enhanced workload placement, dynamic policy-based
• Support for docker-registry deployed containers
• Runtime updates to service and resource topologies
• CI and CD support
• safe monitored dynamic service updates
• TOSCA support
• Support for VMware and CloudStack
• User interface to visualise resource and services relationships
4.2.1.6 T-NOVA
T-NOVA is an integrated project co-funded by the European Commission (7FP) where
the objective is to design and implement an integrated management architecture
containing an orchestrator platform. This orchestrator platform should be capable of
provisioning, managing, monitoring and optimizing Virtualized Network Functions
(VNFs) over Network/IT infrastructures. Additionally, it exploits and extends Software
Defined Networking (SDN) aspects in the way of focusing on the Openflow technology
for efficient management of network resources (supports network slicing, traffic
redirection and QoS provision).
As depicted in Figure 14, the overall architecture of T-NOVA is basically composed of
three platforms: the Orchestrator platform, the Infrastructure Virtualization and
Management (VIM) platform and the Marketplace. The Orchestrator Platform, modules
and interfaces are shown in Figure 11, is in charge of orchestrating VNFs by offering an
automated deployment and configuration of VNFs and a federated management and
optimization of networks and IT resources for accommodating network functions.
Internally, the Orchestrator platform contains multiple submodules:
• Resource Repository module: includes a network topology service in order to
assist the resource mapping procedure
• Resource Mapping module: provides a resource mapping algorithm to decide on
resources and placement for the VNF deployment
• Connectivity Management module: instantiation of virtual networks and QoS
provision to meet service level agreements (SLAs)
• Cloud Management module: allocation of computing and storage resources for
NF instantiation (e.g. on OpenStack)
• NF Management modules: parameterizes the deployed NFs
• Monitoring and Optimization module: monitors networking and
computing/storage assets to optimize them (VM resize/migration, virtual
network reconfiguration)
• High Availability module: performs detection and forecast of operational
anomalies. It triggers fault mitigation procedures (e.g. VM migration, network
re-planning) once detected an anomaly.
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Figure 14. High-level view of overall T-NOVA System Architecture
To enable communication between the orchestration plane and other planes (e.g. service
plane, management plane) it provides a set of northbound and southbound interfaces.
Northbound interfaces expose functionalities like network and cloud resource listing,
function deployment, service provisioning, service monitoring and reconfiguration, and
service teardown. Southbound interfaces are used for the allocation and management
process of network and cloud resources, in particular, interfacing with OpenStack’s
RESTful API, Open Cloud Computing Interface (OCCI) or even with a dedicated SDN
controller.
Figure 15. T-NOVA Orchestrator platform, modules and itnerfaces
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In terms of the ETSI NFV specification the Infrastructure Virtualisation and
Management (IVM) layer can be seen as the VIM. It provides the following
functionalities:
• Support separation of control plane, data plane and network programmability
• Allocation, management and release of resource. Additionally, it offers resource
isolation, optimisation and elasticity.
• Controlling horizontal and vertical scaling
• Provides physical network connectivity between servers by using L2 Ethernet
switched networks (subnets)
• Managing networks by controlling virtual switch or real physical SDN-capable
switches
• Managing L3 networks for interconnecting L2 subnets
The Marketplace, in turn, is in charge of offering Network Services and Functions to the
community where Services can be published, acquired and instantiated on-demand.
These Network Services and Functions are created by a variety of developers and
allows to select prepared services and virtual appliances that best match specific needs.
This attracts new market entrants to browse through the catalogue of available Services
to find existing Services and to simplify Service creation as well. Mainly, the
marketplace satisfies three functionalities:
• Publication of resources and NF advertisement
• VNF discovery, resource trading and service matching
• Customer-side monitoring and configuration of the offered services and
functions
5G-SONATA
This project is part of the 5G-PPP framework recently launched by the European
commission. At the moment of writing this deliverable there were no public documents
available describing what is the innovation which this project will bring in the
management and orchestration of network functions. The only known information are
the ones mentioned on their public website [5G_SONATA]. They aim to:
• Reduce time-to-market of networked services
• Optimize resources and reduce costs of service deployment and operation
• Accelerate industry adoption of software networks
5G Exchange
The 5G Exchange project [5G_EX], launched in the end of 2015, is an European-funded
and Ericsson-headed project aiming to deliver a “unified European 5G Infrastructure
service market that integrates multiple operators and technologies”. It focuses on
supporting cross-domain services orchestration over multiple administrations or multidomain single administrations being compatible with NFV and SDN technology. So the
objective of this project is to design and specify architecture, mechanisms, algorithms
and enablers for automated and fast provisioning of infrastructure services in a multidomain/multi-operator 5G environment. A proof of concept prototype should be
implemented by building a working end-to-end system.
Since the project is in a very early state, the documentation is very limited at the
moment of writing this deliverable but a first approach of the 5G Exchange architecture
was already published as depicted in Figure 16.
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Figure 16. 5G Exchange architectural approach
The main components of the 5G architecture are:
• Runtime Engine: The Runtime Engine is in charge of managing networks and
Virtual Machines across multiple domains.
• Exchange of Functions: This component manages service components across
multiple domains
• Exchange of Information and Control: This enforces interworking, SLAs,
mapping and automatic management of service and network functions.
4.2.2 NUBOMEDIA approach beyond SotA
This section serves as a comparison between the orchestration solutions described above
and the requirements that the NUBOMEDIA Media Plane management components
need to support. For this reason, it is proposed a taxonomy that includes generalized
features of orchestration systems and NUBOMEDIA-related requirements where each
of the solutions is compared to. To see if a specific feature is supported or not, it can be
differentiated between the following cases:
• Yes, means that this feature is supported out of the box. It requires no further
adoptions and is mentioned or even described in the documentation of the
corresponding project.
• No, means that this feature is not supported at all. Since most of the projects are
fairly new at the moment of doing this comparison, the projects are continuously
under development with frequently improvements and new features, so it might
be that any not-supported feature will be supported soon, a new feature is not yet
announced to the community or at least it is not mentioned explicitly.
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5G-SONATA
5G Exchange
no documentation
(not yet released)
T-Nova
Hurtle / MCN
Cloudify
Juju
Tacker
no documentation
NFV compliancy
Provides NFVO
Provides VNFM
License type
Open source
Supported cloud infrastructures
OpenStack
Resource abstraction layer
Compute
Storage
Volume
Network
Management tools
CLI
API
GUI
Core service layer
Identity service
Image repository
Charging and Billing
Logging
Monitoring layer
Basic metrics
Custom metrics
Elasticity management layer
Scaling
Manual
Automatic
Scale-in
Termination
rules
Placement of Media Elements
OpenBaton
Ext, stands for extendable and indicates if a not-supported feature is supportable
by extending, integrating or replacing (re-implementing) a specific component
that is in charge of this functionality. Basically, this requires a guideline for
developing components with extended functionalities or a comprehensive
documentation explaining architectural components and interfaces, relations
between them and workflows. In case that a possible extension - to support a
feature - is not mentioned explicitly or the documentation is not comprehensive
enough or even absent it is seen as not extendible and therefore not supported.
OpenMANO
•
Yes Yes No No No No No
Yes Yes Yes No Yes Yes Yes
No Yes Yes Yes No No Yes
Yes Yes Yes Yes Yes Yes Yes
Yes Yes Yes Yes Yes Yes Yes
Yes
No
No
Yes
Yes
Ext
Ext
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Ext
Ext
Yes
Yes
No
No
Yes
Yes Ext Yes Yes Yes No No
Yes Yes Yes No Yes Yes No
Yes Yes Yes Yes Yes Yes No
Yes
Yes
No
Yes
No
No
No
Yes
Yes
Ext
Yes
Yes
Yes
Yes
No
No
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Ext
Ext
No
No
No
No
Yes
Yes
Yes
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
Ext
No
No
No
No
No
Table 1. Comparison of several orchestration solutions against several features
As you can notice from the previous table, none of the existing solution was satisfying
all the NUBOMEDIA requirements. Furthermore, it is important to notice that the
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selection was done during release 4 when most of the presented tool did not have source
code to be downloaded and executed. Nevertheless, in parallel Fraunhofer FOKUS and
TU Berlin launched a new NFV compliant platform (OpenBaton) which was providing
almost all the features required in NUBOMEDIA. For this reason, it was decided to
invest additional effort coming from WP3 related tasks in extending this platform for
implementing the additional features required in NUBOMEDIA.
4.2.2.1 OpenBaton
OpenBaton is an ETSI NFV compliant Network Function Virtualization Orchestrator
(NFVO) implemented by Fraunhofer FOKUS and TU Berlin being capable of
interoperating with VNF Managers implemented by third parties. This project started in
the beginning of 2015 (in parallel with the NUBOMEDIA rel.4) and is highly active
with almost weekly improvements and bugfixes. OpenBaton is completely open source
and claims to be fully compliant with the ETSI Management and Orchestration
specifications. Therefore, it includes a consistent set of features and tools enabling the
deployment of VNFs on top of cloud infrastructures.
Since OpenBaton is fully compliant with the ETSI NFV MANO specifications, the
architecture of OpenBaton is also aligned to it. Nevertheless, a simplified high-level
architectural overview of OpenBaton can be found in ¡Error! No se encuentra el
origen de la referencia. Following this architecture, three main components can be
pointed out:
• Orchestrator (NFVO): The Orchestrator is in charge of processing the key
functionalities of the MANO architecture. For this reason it maintains an
overview on the infrastructure allowing to register and manage multiple Vim
instances (also known as Network Function Virtualization Infrastructure Point
of Presence or simply NFVI-PoP) dynamically in order to support the
deployment of VNFs across multiple datacenters and regions. This enables the
NFVO to take care of validation and authorization of NFVI resource requests
from the VNF Manager. It is also responsible for the Network instantiation and
Network Service instance lifecycle management (e.g. update, query, scaling,
collecting performance measurement results, event collection and correlation,
and termination). The instantiation of VNFs itself is done in coordination with
VNF Managers.
• VNF Managers (VNFMs): The VNF Manager is in charge of managing the
lifecycle (e.g. instantiate, start, configure, modify, stop, terminate,) of VNF
instances. Therefore, each VNF of a certain type is explicitly assigned to a
specific VNF Manager whereas a VNF Manager might be capable of managing
multiple VNFs of the same type or even of different types. Depending on the
type of the VNF to handle, the VNF Manager might be able of handling generic
common functions applicable to any type of VNF or in case of handling specific
VNFs the VNF Manager needs to provide specific functionalities for their
lifecycle management. A list of main functionalities that each VNF Manager
have to provide is listed in the following:
o VNF instantiation and configuration
o VNF instance modification
o VNF instance scaling out/in and up/down
o VNF instance termination
• Virtualized Infrastructure Manager (VIM): The VIM is in charge of controlling
and managing NFVI compute, storage and network resources. A VIM might be
capable of handling a certain type of NFVI resource (e.g. compute-only, storageonly, networking only) or even multiple types. At the moment OpenBaton
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supports OpenStack as a first example of a VIM for NFV PoPs. Nevertheless,
thanks to the “pluggability” it is easily possible to support other types of VIMs
by implementing another plugin (vim-driver) that supports the type of
infrastructure of choice. Anyway, the main tasks of a VIM of any type is listed
in the following:
o Orchestrating the allocation, upgrade, release and reclamation of NFVI
resources
o Managing inventory related information of NFVI hardware and software
resources, and discovery of the capabilities and features of such
resources
o Management of the virtualized resource capacity
o Management of software images (add, delete, update, query and copy)
o Collection of performance and fault information
o Management of catalogues of virtualized resources that can be consumed
from the NFVI
Figure 17. High-level Architecture of OpenBaton
Additionally, as shown in Figure 18, OpenBaton provides already a first version of a
VNF Manager, called generic VNFM, supporting generic functionalities. The generic
VNFM is tightly coupled with OpenBaton’s EMS which runs as a software within
deployed Network Functions. Thanks to the EMS, the generic VNFM is capable of
executing specific configuration scripts within virtual machine instances at runtime.
This VNF Manager is instantiated on demand by the NFVO where the VNF Manager
can request from the NFVO the instantiation, modification, starting and stopping of
virtual services (or even may request the specific VIM instance directly). Apart from
generic functionalities the generic VNFM can be easily extended and customized to
support specific types of VNFs.
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Figure 18. High-level Architecture of OpenBaton and the Generic VNFM inlcuding the EMS
Furthermore, OpenBaton provides some interfaces and frameworks that makes it easy to
align the NFVO and VNFMs to certain needs. Some of the most important aspects are
listed below:
• Plugin-mechanism: Thanks to the plugin-mechanism, plugins can be easily
added to support other types of VIMs and monitoring frameworks. This can be
done by using the provided plugin-sdk and implementing the corresponding
interface that the plugin needs to satisfy in order to support the functionalities
that are mandatory for communicating and managing either the VIM or the
monitoring framework.
• openbaton-client: The openbaton-client, also known as the SDK, supports some
basic functionalities to communicate with the NFVO. This includes the
management of NSDs, NSRs, VIM instances, Virtual Links, Images, Events and
Configurations.
• Vnfm-sdk: The vnfm-sdk, providing an AbstractVNFM, can be used to
implement a new VNFM with specific functionalities related to specific VNFs.
Depending on the communication type of choice it provides also more specific
AbstractVNFMs with a built-in communication between NFVO and VNFM
(e.g. AbstractVnfmSpringAmqp, AbstractVnfmSpringReST). In the simplest
case it is just important to implement the methods that are called during the
lifecycle management (e.g. instantiate, modify, configure, start, stop, terminate,
etc). But also more complex VNF Managers can be implemented and integrated
easily supporting specifc management of VNFs, additional entities, monitoring,
autoscaling and fault management – just to mention a few options. The
framework for implementing new VNFMs provides also a built-in helper, called
VNFMHelper, for supporting a simplified mechanism to communicate with the
NFVO in order to send and receive specific messages that may include requests
(action-based) to the Orchestrator itself.
4.2.3 NUBOMEDIA outcomes
Extensions to the OpenBaton NFVO and generic-VNFM have been directly pushed on
the OpenBaton repositories. The additional components are currently part of the tubNUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
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nubomedia github repository [TUB_GITHUB] and we’ll be integrated in the
mainstream repository as soon as their implementation is completed and properly
tagged.
4.2.4 References
Referenced websites
[5G_EX] https://5g-ppp.eu/5gex/
[5G_SONATA] http://www.sonata-nfv.eu/
[CLOUDIFY] http://getcloudify.org/
[HURTLE] http://hurtle.it/
[JUJU] https://juju.ubuntu.com/get-started/
[OPENBATON.ORG] http://openbaton.org
[OPEN_MANO] https://github.com/nfvlabs/openmano
[TACKER] https://wiki.openstack.org/wiki/Tacker
[TNOVA] http://www.t-nova.eu/
[TUB_GITHUB] https://github.com/tub-nubomedia
Referenced papers or books
[ETSI_WP] ETSI. Network functions virtualisation - introductory white paper, October
2012. At the SDN and OpenFlow World Congress, Darmstadt Germany.
[MANO] Network Function Virtualization Management and Orchestration. (2014).
Retrieved December 14, 2015 from http://www.etsi.org/deliver/etsi_gs/NFVMAN/001_099/001/01.01.01_60/gs_nfv-man001v010101p.pdf
4.3 PaaS for Real-Time Multimedia Applications
Platform as a Service (PaaS) has been one of the vibrant topics in recent cloud
computing technology trends. It is foreseen to be a game changer for next generation
Real-Time multimedia applications. However, unlike Infrastructure as a Service (IaaS)
which is making efforts in consolidating and standardization, the PaaS market is highly
fragmented with sets of varying ecosystem capabilities.
The National Institute of Standards and Technology (NIST) Definition of Cloud
Computing defines PaaS as “the capability provided to the consumer to deploy onto the
cloud infrastructure consumer-created or acquired applications created using
programming languages, libraries, services, and tools supported by the provider. The
consumer does not manage or control the underlying cloud infrastructure including
network, servers, operating systems, or storage, but has control over the deployed
applications and possibly configuration settings for the application-hosting
environment” [NIST_DEF]
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From this well-known and often cited definition, one can already see that the term PaaS
be interpreted from different perspectives. In fact, definitions are widely spread and
often conducted from contrasting perspectives resulting in diverging PaaS categories
missing a common consensus. The result is a crowded market of PaaS offerings that
sometimes provide completely different capabilities [FORRESTER_REP]. In general,
the definition only requires the ability to deploy applications which have certain
dependencies that are supported by the platform environment. Moreover, it demands the
abstraction from the underlying cloud infrastructure while possibly granting access to
unspecified configuration settings of the PaaS environment.
The NIST defined a Cloud Computing Reference Architecture as presented in Figure
19.
Figure 19 NIST PaaS Reference Architecture
Most notably, this architecture, explicitly defines 5 different roles:
• Cloud Consumer: a person or an organization using a service from the Cloud
Provider.
• Cloud Provider: a person or (more likely) an organization making a service
available to interested consumers
• Cloud Auditor: a person or (more likely) an organization conducting an
assessment of cloud services, information system operations, performance and
security of the cloud implementation
• Cloud Broker: a person or (more likely) an organization that manages the use,
performance and delivery of cloud services, including negotiations between
• Cloud Providers and Cloud Consumers
• Cloud Carrier: an intermediate organization that provides connectivity and
transport of cloud services from Cloud Providers to Cloud Consumers
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While this model clearly describes the various players in a PaaS environment, it is fits
more to a description of the PaaS ecosystem. In addition, the model very abstract when
it comes to the actual software architecture of the PaaS implementation.
As an evolution for the NIST reference model, [GARTNER_MODEL] defines a
reference model of the core components and fundamental architecture of a
comprehensive implementation as presented in Figure 20
Figure 20 Gartner Refernece Archiecture for PaaS
In particular, this model distinguishes between:
• hardware and infrastructure (network, storage etc.) services that correspond to
an IaaS solution
• a sound Technology Base dealing with scalability and elasticity, SLA
management, billing, monitoring etc. – in brief with all the fundamental
infrastructure services that will be required for any PaaS-based application
• the actual PaaS Layer providing APIs and tools allowing the implementation,
integration, delivery and management of specific applications.
Gartner’s reference model just mentions the user and the provider as 3rd party entities
without going into details as to what they might need. However, when it comes to
evaluating a potential PaaS vendor or implementing a PaaS stack, we prefer the
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following model that was adopted from the PaaS Reference Architecture by Forrester
Research.
Figure 21 Forrester Research PaaS Reference Architecture
The Forrester based reference architecture for the PaaS shows what is required both
from the side of the user/subscriber (green parts) as well as by potential Independent
Software Vendors (ISV) that will implement applications on top of the PaaS platform
(red parts). The blue parts of the diagram concentrate on what actually has to be
implemented by the PaaS provider, most notably (from bottom to top):
• a sound layer ensuring security and data protection across the entire platform
• an application platform based on which specific applications will be
implemented, delivered, and maintained; including appropriate middleware and
persistence
• an integration platform by means of which various components and services can
be managed and orchestrated, data can be exchanged between these etc.
• a UI framework based on which (Web or mobile) user interfaces can be
implemented which will then be used by the users/subscribers to access to
application
• a model platform that allows for the integration of the PaaS services with other
applications and services, typically via BPMS
• some selected application components as they might be provided by the PaaS
provider directly, these could serve as demos on how to use the system or really
provide useful functionality that can be utilized by the ISVs when implementing
other applications.
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All these aspects (referring to boxes in dark blue) also apply to a SaaS architecture. In
order to provide a PaaS platform, however, some more work is required as shown in the
light blue boxes of the diagram:
• Towards the user/subscriber methods and tools for purchasing, reporting, user
management etc. are required, of course. But even more, mechanisms will be
needed by means of which the subscriber can configure the application in terms
of their own metadata, orchestration with other apps being used, and even the
integration with legacy systems.
• The ISV, on the right hand side of the diagram, will require tools for Software
lifecycle management, including a development environment and sandbox area
for testing purposes, but also an tools for marketing, contracting, billing etc.
such that the ISV can actually reach out to customers, sell the application and
charge for it.
In a certain sense, a PaaS platform thus has to provide more features than a SaaS
platform as the latter is typically focused on a single application thus not requiring
things like service orchestration or billing for multiple ISVs. Hence, implementing a
PaaS platform sounds like a challenging task and requires a sound software architecture
for sure.
Nevertheless, to be able evaluate the different PaaS for Real-Time applications, we shall
focus on some base criteria that form a common ground model to proceed in analyzing
the different PaaS solutions. These parameters include deployment models, platform
and management.
Deployment Features:
The NIST Definition of Cloud Computing [15] defines four types of deployment
models for cloud computing including PaaS.
• Private PaaS. The private PaaS is provisioned for exclusive use by a single
organization comprising multiple consumers (e.g., business units). It may be
owned, managed, and operated by the organization, a third party, or some
combination of them, and it may exist on or off premises.
• Community PaaS. The cloud infrastructure is provisioned for exclusive use by a
specific community of consumers from organizations that have shared concerns
(e.g., mission, security requirements, policy, and compliance considerations). It
may be owned, managed, and operated by one or more of the organizations in
the community, a third party, or some combination of them, and it may exist on
or off premises.
• Public PaaS. The cloud infrastructure is provisioned for open use by the general
public. It may be owned, managed, and operated by a business, academic, or
government organization, or some combination of them. It exists on the
premises of the cloud provider.
• Hybrid PaaS. The cloud infrastructure is a composition of two or more distinct
cloud infrastructures (private, community, or public) that remain unique entities,
but are bound together by standardized or proprietary technology that enables
data and application portability (e.g., cloud bursting for load balancing between
clouds).
Also included in deployment features are other criteria such as open-source, method of
isolation (containers or virtual machines), number of geographical data regions, pricing
options, and whether or not a free option is available.
Platform:
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The platform is the main deliverable of a PaaS solution which includes the application
hosting environment delivered as a service. The best approach in choosing a PaaS starts
with the people who actually will be using it: developers. Most important, developers
want to know that a given solution will support the software stack of their choice, and
these criteria should be at the fort front of any PaaS evaluation.
Two stacks of components are crucial – the runtime stack and the service stack. Both
stacks can be mingled to users through bindings. These bindings can be set as
environment variables that include important properties of the service for example
service routes, credentials and other configuration information.
• Runtime Stack: includes the basic runtime offered by the PaaS such as
programming languages that application can be written in. Many applications
also depend on middleware and frameworks that may be hosted by the PaaS.
• Service Stack: includes two types of services namely native and add-on service
stacks. Native service stacks are hosted on and operated by the PaaS platform
e.g. latency and performance critical core services, databases. Add-ons are 3rd
party cloud services that developers can use to immediately extend their
applications with a range of functionality such as data stores, logging,
monitoring and other utilities.
Extensibility:
Another key functionality of the PaaS platform is extensibility. A modern PaaS should
enable developers to add own packages of services or runtimes to the PaaS
environment, which can operate as isolated entities that can generate any of the service
or runtime stacks capabilities.
Scalability:
An important aspect in considering the PaaS platform is also scalability. Most PaaS
solutions provide horizontal and/or vertical scaling of applications.
• Horizontal Scale: also referred to as Scale Out is to add more nodes or resources
to the network of nodes that are running the application. The advantage of
horizontal scaling is that load is distributed amongst each node in the network
and also it provides high availability. Also, it does not require downtime as you
are adding more nodes without affecting the ones that are running the
application
• Vertical Scale: or Scale Up is to add more power (CPU, RAM, Disk) to the
existing node running the application. The advantage of vertical scale is that
many servers do not need to be managed. The disadvantage is that it requires
downtime as capacity is being added to a single running node. Also, potential
risk of having a single point of failure if in case that single running node goes
down.
Management:
In addition to the deployment model and the platform of the PaaS solution, the last key
aspect is with regards to the manageability. The management allows control over the
deployed applications and the configuration settings of the platform. It also provides the
abilities to deploy and manage the lifecycle of the applications. This encompasses
pushing, starting, and stopping of applications. Moreover, the provisioning of all native
services and add-ons is initiated from the management tier. All available configuration
and administration settings for the applications and the PaaS environment can also be
controlled. This includes a wide range of functionality like scaling, logging, down to the
creation of domain routes and environment variables. The management layer also
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covers the resource usage monitoring that is relevant for billing and scaling decisions.
All these functionalities are controlled by the management interface. This interface can
be a RESTful API, console-based or driven via web user interfaces.
4.3.1 Description of current SoTA
After looking at the common criteria for analyzing PaaS solutions, the next subsection
describes some popular PaaS solutions widely used around the world.
4.3.1.1 Cloud Foundry PaaS
Cloud Foundry is an open source PaaS originally developed by VMware and now
owned by Pivotal Software [FOUNDRY]. It provides subscribers with a choice of
clouds, developer frameworks and application services.
Figure 22 Cloud Foundry Components Overview
Cloud Foundry components include a self-service application execution engine, an
automation engine for application deployment and lifecycle management, and a
scriptable command line interface (CLI), as well as integration with development tools
to ease deployment processes. Cloud Foundry has an open architecture that includes a
build-pack mechanism for adding frameworks, an application services interface, and a
cloud provider interface.
• Router: routes incoming traffic to the appropriate component, usually the Cloud
Controller or a running application on a DEA node.
• OAuth2 Server (UAA) and Login Server: work together to provide identity
management.
• Cloud Controller: is responsible for managing the lifecycle of applications.
When a developer pushes an application to Cloud Foundry, they are targeting
the Cloud Controller. The Cloud Controller then stores the raw application bits,
creates a record to track the application metadata, and directs a DEA node to
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•
•
•
•
•
•
stage and run the application. The Cloud Controller also maintains records of
orgs, spaces, services, service instances, user roles, and more.
Health Manager (HM9000): has four core responsibilities:
o Monitor applications to determine their state (e.g. running, stopped,
crashed, etc.), version, and number of instances. HM9000 updates the
actual state of an application based on heartbeats and droplet exited
messages issued by the DEA running the application.
o Determine applications’ expected state, version, and number of
instances. HM9000 obtains the desired state of an application from a
dump of the Cloud Controller database.
o Reconcile the actual state of applications with their expected state. For
instance, if fewer than expected instances are running, HM9000 will
instruct the Cloud Controller to start the appropriate number of instances.
o Direct Cloud Controller to take action to correct any discrepancies in the
state of applications.
HM9000 is essential to ensuring that apps running on Cloud Foundry remain
available. HM9000 restarts applications whenever the DEA running an app shuts
down for any reason, when Warden kills the app because it violated a quota, or
when the application process exits with a non-zero exit code.
Application Execution (DEA): Droplet Execution Agent manages application
instances, tracks started instances, and broadcasts state messages.
Blob Store: holds application code, build packs, droplets
Service Brokers: Applications typically depend on services such as databases or
third-party SaaS providers. When a developer provisions and binds a service to
an application, the service broker for that service is responsible for providing the
service instance.
Message Bus (NATS): Cloud Foundry uses NATS, a lightweight publishsubscribe and distributed queuing messaging system, for internal
communication between components.
Metrics Collector and App Log Aggregator: collects logging and statistics, while
the metrics collector gather metrics from the components. Operators can use this
information to monitor an instance of Cloud Foundry.
Deployment features:
Cloud Foundry is a public PaaS which is designed to be configured, deployed,
managed, scaled, and upgraded on any cloud IaaS provider. This is achieved by
leveraging BOSH, an open source tool for release engineering, deployment, lifecycle
management, and monitoring of distributed systems.
Platform:
From the platform runtime parameter, Cloud Foundry offers 8 programming languages
with which applications can be deployed. These languages include Go, Groovy, Java,
Node, PHP, Python, Ruby, and Scala. In addition it offers Tomcat as middleware
platform for deployment of Java (version 6, 7) based applications. In addition, it
provides the following frameworks: Grails (for Groovy), Play (Java), Rails (Ruby),
Sinatra (Ruby) and spring for the Java runtime.
Scalability:
In terms of scalability, Cloud Foundry provides both horizontal and vertical scalability.
Horizontally scaling an application creates or destroys instances of the application.
Incoming requests to that application are automatically load balanced across all
instances of the application, and each instance handles tasks in parallel with every other
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instance. Adding more instances allows the application to handle increased traffic and
demand. Vertically scaling an application changes the disk space limit or memory limit
that Cloud Foundry applies to all instances of the application.
Management:
The management layer of Cloud Foundry is provided through a command line interface.
With the command line interface, users are able to deploy, start and stop applications,
access logs, configure application deployment parameters, just to name a few.
4.3.1.2 Red Hat OpenShift Origin
OpenShift is a PaaS solution offered by Red Hat [SHIFT]. Two types of PaaS
deployment models are offered by Red Hat namely:
• Public PaaS: OpenShift Online is Red Hat’s hosted public PaaS that offers an
application development, build, deployment, and hosting solution in the cloud. It
lets applications scale automatically in an elastic cloud environment, ensure
faster time to market, and it is freely available online for registered users to host
their applications
• Private PaaS: OpenShift Enterprise takes the same open source PaaS platform
that powers the OpenShift Online hosted service and packages it for customers
who want an on premise or private cloud deployment.
The rest of this section however will concentrate on Red Hat’s OpenShift Origin
because this is the PaaS solution fuelling both the Online and Enterprise version.
OpenShift mission statement is geared principally towards developers. Their main
objective is to ease of use and complexity involved in deploying applications on the
cloud. OpenShift was recognized by InfoWorld as one of 2015’s best tools and
technologies for developers, IT professionals, and businesses [INFO_WORLD].
Figure 23 OpenShift Origin Architecture Overview
OpenShift provides a micro-services based architecture of smaller, decoupled units that
work together. The architecture is designed to expose underlying Docker and
Kubernetes concepts as accurately as possible, with a focus on easy composition of
applications by a developer. Docker is an open platform for developers and sysadmins
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to build, ship, and run distributed applications, whether on laptops, data center Virtual
Machines (VMs), or on the cloud. OpenShift Origin uses it as containers for running
applications. Kubernetes is an open source orchestration system for Docker containers
developed by Google. OpenShift Origin uses Kubernetes for scheduling and packaging
Docker containers onto nodes in a compute cluster and manages it workloads to ensure
that their state machines reflects the users declared intentions.
• Core services: these include services such as authentication for security of the
system; Data Store for keeping persistent metadata of PaaS use’s applications
available on given nodes and their capabilities; Scheduler service for
determining placement of new containers on nodes; Management and replication
services to ensure that a specific number of containers (“replicas”) are running at
all times.
• Containers: are the basic units of the applications which uses the concept of
Linux Container Technology. This is basically a lightweight mechanism for
isolating running processes, such that they are limited to interacting with only
designated resources (processes, files, network, database, etc.). OpenShift Origin
uses Docker containers which are based on Docker images. A Docker image is a
binary that includes all of the requirements for running a single Docker
container, as well as metadata describing its needs and capabilities.
• Node: is a worker machine which may be a Virtual Machine (VM) or physical
machine. Each node has the services necessary to run Pods (a group of
containers that make up the application) and is managed by the core
components. The node also provides network proxy and load balancing services
for scaling the traffic it receives amongst all replicas.
• Persistent data: is used to store custom Docker images for building and
deploying user’s applications.
Deployment features:
As mentioned above, OpenShift Origin is deployed as public and private PaaS
Platform:
From the platform runtime parameter, OpenShift Origin offers officially 6 programming
languages with which applications can be deployed. These languages include Java,
Node, PHP, Python, Ruby, and Pearl. However, since it is a community driven project,
there are many more language support coming from the open source community. In
addition it offers JBoss and Tomcat (Java) and Zend Server (PHP) as middleware.
Database support (MongoDB, MySQL, PostgreSQL, SQLite). In addition, it provides
the following frameworks: Django and Flask (Python), Drupal (PHP), Rails (Ruby),
Vert.x (Java) and spring for the Java runtime. To facilitate usage for developers,
OpenShift Origin provides numerous quick start templates with pre-created code
repositories that allow developers to instantly deploy their favorite application
frameworks in one-click.
Scalability:
In terms of scalability, OpenShift Origin provides both horizontal and vertical
scalability. Horizontal scaling for applications is accomplished using HAProxy as a
load-balancer. Whenever the platform gets a scaling request, it performs all the
necessary steps to manage a new instance and configures HAProxy. Vertically scaling
with OpenShift is accomplished by providing bigger containers, thus securing more
resources.
Management:
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The management layer of OpenShift Origin is provided through a command line
interface web console and REST APIs. The REST APIs expose each of the core service
functionality. Users make calls to the REST API to change the state of the system.
Controllers use the REST API to read the user’s desired state, and then try to bring the
other parts of the system into sync. The controller pattern means that much of the
functionality in OpenShift is extensible. By customizing those controllers or replacing
them with your own logic, different behaviours can be implemented. From a system
administration perspective, this also means the API can be used to script common
administrative actions on a repeating schedule. Those scripts are also controllers that
watch for changes and take action. OpenShift makes the ability to customize the cluster
in this way a first-class behaviour.
4.3.1.3 Google App Engine
Google App Engine [APP_ENGINE] is a PaaS platform for developing and hosting
web applications in Google-managed data centers. Applications are sandboxed and run
across multiple servers. Integrated within App Engine are the Memcache and Task
Queue services. Memcache is an in-memory cache shared across the AppEngine
instances. This provides extremely high speed access to information cached by the web
server (e.g. authentication or account information).
Task Queues provide a mechanism to offload longer running tasks to backend servers,
freeing the front end servers to service new user requests. Finally, App Engine features
a built-in load balancer (provided by the Google Load Balancer) which provides
transparent Layer 3 and Layer 7 load balancing to applications.
Figure 24Google App Engine High Level Overview
Deployment Features:
Google App Engine is a public PaaS and free up to a certain level of consumed
resources. Fees are charged for additional storage, bandwidth, or instance hours
required by the application.
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Platform:
Google App Engine provides to developers four languages for developing applications;
Java, Python, PHP and Go. The Java environment supports other languages that make
use of the JRE and there is a SDK for each of the four main supported languages as well
as a plugin for Eclipse. For frameworks, Django and Webapp2 for Python are provided.
Regarding service stacks, Google App Engine provides native services and APIs such as
Google Cloud SQL, Datastore, Storage and a user authentication API which are
common to many applications.
Scalability:
In terms of scalability, Google App Engine provides both auto scaling and vertical
scalability. It provides managed infrastructure and runtime environments that are
guaranteed to scale, but only if the applications fit the restrictions of Google App
Engine
4.3.1.4 Salesforce Heroku
Heroku is one of the early PaaS providers [HEROKU]. It is based on a managed
container system, with integrated data services and a powerful ecosystem, for deploying
and running modern applications. Heroku is ideal for quick deployments and fits a wide
range of distributed applications. Applications that are run from the Heroku server use
the Heroku DNS Server to direct to the application domain. Each of the application
containers or dynos are spread across a "dyno grid" which consists of several servers.
Heroku's Git server handles application repository pushes from permitted users
Deployment Features:
Heroku is available as a public PaaS, located in Ireland - Dublin and in USA-Northern
Virginia deployed on Amazon Web Service IaaS
Platform:
Heroku runs applications inside dynos, which are smart containers on a reliable, fully
managed runtime environment. It provides to developers nine languages for developing
applications; Java, Python, PHP, Go, Groovy, Clojure, Node, Ruby and Scala. The
system and language stacks are monitored, patched, and upgraded, so it's always ready
and up-to-date. The runtime keeps apps running without any manual intervention. For
frameworks, Django, Flask, Grails, Play and Rails are available. Developers deploy
directly from popular tools like Git, GitHub or Continuous Integration (CI) systems.
Regarding service stacks, Heroku supports Cloudant, Couchbase Server, MongoDB and
Redis databases in addition to its standard PostgreSQL, both as part of its platforms and
as standalone services. Outstanding difference to the other PaaS solutions is the huge
number of add-on services (179) provided. Heroku Elements let developers extend their
applications with add-ons, customize their application stack with Buildpacks and
jumpstart their projects with Buttons. Heroku provides two fully-managed data service
Add-ons: Heroku Postgres and Heroku Redis.
Scalability:
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Heroku uses a process model that scales up or down applications instantly from the
command line or Dashboard. Each application has a set of running dynos, managed by
the dyno manager
Management:
The management layer of Heroku is provided through an intuitive web-based
Dashboard that makes it easy to manage applications and monitor application
performance. Also available is a command line interface.
4.3.1.5 Comprehensive comparison of PaaS solutions
The above sections have briefly described some of the popular PaaS solutions in the
market today. To give a complete description of all solutions will span this document to
eternity. Nevertheless, this section provides a close up side by side comparison of the
above described and other PaaS solution with the criteria model provided above. This
comparison is based on [SOLUTIONS_REVIEW] report on 2016 Comparison Matrix
Report on PaaS. For simplicity's sake, the Solutions Review PaaS Comparison Matrix
only includes runtimes, frameworks, middleware, and services that are native to, or
fully supported by, each solution. However, it should be noted that 60 percent of the
solutions listed are extensible, and can add new runtime and framework support via
community buildpacks at varying degrees of difficulty to the user.
Figure 25 Solutions Review report on 2016 Comparison Matrix Report
Virtual
Machines
Linux
Containers
Metered
Pricing
Monthly
Pricing
Free option
Infrastructure
Cloud
Open Source
Pivotal
Foundry
Hosting
Name
Table 2 PaaS comparison Deployment Features
private
yes
no
yes
yes
yes
yes
private
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Red Hat OpenShift
Online
Red Hat OpenShift
Enterprise
Salesforce Heroku
public
yes
no
yes
yes
yes
yes
private
yes
no
yes
yes
yes
yes
public
no
no
yes
yes
yes
yes
Google App Engine
Microsoft Azure
public
public
no
no
yes
yes
no
no
no
yes
yes
yes
yes
yes
IBM Bluemix
Centurylink Appfog
public
Public
private
public
no
no
yes
yes
no
yes
yes
no
yes
yes
no
no
no
yes
no
no
yes
no
no
yes
no
no
yes
yes
Engine Yard
Amazon
Beanstalk
Elastic
&
public
Europe,
America
private
North
Ireland
USA
Europe, USA
Asia,
Europe,
USA,
Australia,
Brazil
Europe, USA
Asia,
Europe, USA
Europe,
USA,
Australia, Brazil
Europe,
USA,
Australia, Brazil
Ruby
yes
yes yes
yes yes yes
yes
yes
yes
yes
yes
yes
yes
Yes
yes
yes
yes
yes
yes
Yes
yes
yes
yes
yes
yes yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes yes
yes
yes
yes
yes
yes yes
PHP
yes yes yes
Node
yes
jRuby
yes
Java
yes yes yes
Unicorn
yes
Python
Google App Engine
Microsoft Azure*
IBM Bluemix*
Centurylink Appfog
Engine Yard
Amazon Elastic Beanstalk
yes
Tomcat
yes
Puma
Pivotal Cloud Foundry
Red
Hat
OpenShift
Online*
Red Hat OpenShift
Enterprise 17
Yes
Salesforce Heroku*
Groovy
Go
.NET
Clojure
Name
Table 3 PaaS comparison Platform - Runtime
Pivotal Cloud Foundry
Red
Hat
OpenShift
Online*
17
yes
yes
yes
Rack
Passenger
Nginx
Jetty
JBoss
HAProxy
Gunicorn
Name
Table 4 PaaS comparison Platform - Middleware
*=Extensible
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Red Hat OpenShift
Enterprise 18
Salesforce Heroku*
Google App Engine
Microsoft Azure*
IBM Bluemix*
Centurylink Appfog
Engine Yard
Amazon Elastic Beanstalk
yes
yes
yes
yes
yes
yes
yes yes
yes yes yes yes
yes yes yes
yes
yes
yes
yes
yes yes yes yes yes yes
yes yes
yes
yes yes yes
yes
yes
Tomcat
Unicorn
yes
Puma
yes yes
yes
Pivotal Cloud Foundry
Red
Hat
OpenShift
Online*
Red Hat OpenShift
Enterprise 19
yes
Salesforce Heroku*
yes
yes
yes
yes yes
Google App Engine
Microsoft Azure*
IBM Bluemix*
Centurylink Appfog
Engine Yard
Amazon Elastic Beanstalk
yes yes yes yes
yes yes yes
yes
yes
yes
yes
Rack
Passenger
Nginx
Jetty
JBoss
HAProxy
Gunicorn
Name
Table 5 PaaS comparison Platform - Framework
yes
yes
yes
yes
yes
yes yes yes yes yes yes
yes yes
yes
yes yes yes
yes
yes
4.3.2 NUBOMEDIA approach beyond SotA
The last section presented the concept of PaaS and some of the most popular PaaS
solutions and their limitation. This section presents the approach taken for the
NUBOMEDIA project. The NUBOMEDIA project deals specifically with Real-Time
multimedia applications.
An obvious limitation from the solutions presented above is the focus on Real-Time
applications. Their infrastructure is generalized to web application as a whole and little
emphasize on Real-Time Communication services.
One of NUBOMEDIA’s intent is to address this limitation by providing an open source
PaaS platform, which makes it possible for developers to implement and host advanced
multimedia applications.
• Providing service stacks for facilitating the development of Real-Time
applications
18
19
*=Extensible
*=Extensible
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•
•
•
Reducing complexity in deploying Real-Time applications by providing simple
APIs that can be used from the web and programmatically.
Provide cloud Media pipelines which offer specific APIs to define media
processing topologies such as augmented reality filters and visual computing
toolkits for seamless integration
Providing add-ons to support real-time interoperability with other multimedia
networks
With this objectives, and from the analysis of the existing solutions above, we have
identified a couple of requirement the NUBOMEDIA PaaS system will need to meet.
4.3.2.1 Requirements on NUBOMEDIA PaaS Platforms
While reading these requirements, it is worth mentioning to keep in mind the fact that,
the PaaS is somewhere in between IaaS and SaaS. So not only will developers access
services running in the cloud but also they need to be able to ship and manage their
applications via the platform. In addition to standard software engineering principles,
this results in a couple of requirements for PaaS:
•
As many different developers should be served, the platform needs to
be multi-tenant. This not only relates to a clear separation of all data
structures, account data, and service orchestration of all tenants but also
includes ways of managing and setting up new developers easily such as in
the case of evaluation periods.
•
Zero footprint requirement which means that the user should not have to
install any components locally. This, of course, implies that the
NUBOMEDIA PaaS platform is fully designed for the Web supporting
access via the browser and, ideally, mobile devices.
•
Specifically and needless to say that scalability plays a major role in RealTime applications. The PaaS must be able to scale at least horizontally the
deployed application. But the PaaS platform should not just be able to grow
in case of higher demands but also to shrink during off-peak periods. In
other words the platform should be elastic in the sense that resources are
managed wisely depending on demand and it is easy to scale up and down.
•
Given that developers will implement and ship their specific applications via
the PaaS platform, a programming model as well as support for popular
frameworks and programming languages are required. This also includes
mechanisms for software lifecycle management such that application
providers can easily and safely develop, test and ship their applications via
the PaaS platform.
Given these requirements and the numerous open source versions of popularly used
PaaS solution, the intent of NUBOMEDIA is not to yet develop a new PaaS solution
from scratch, but utilise and enhance an existing open source solution to fit our demands
and requirements for PaaS for a Real-Time applications.
For this project, we decided to use the open source version of Red Hat’s OpenShift OpenShift Origin v3. This is the community driven implementation of the PaaS which
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introduces the use of Docker and Kubernetes. In addition to the advantages offered by
Docker and Kubernetes, there are several reasons, why we decided for this software
component:
•
•
•
•
Provision of over the top functionalities for augmented deployment,
orchestration and routing.
Provision of groups of Docker containers called pods that simulate a single
virtual machine with a single IP address, shared file system and common
security settings. This ability provides the advantage to deploy scalable
applications with shared local resources.
Extensibility. Through the REST APIs to core components services,
alternative implementations are applicable.
Flexibly linked feature; Other PaaS platforms are limited to only web
frameworks and rely on external services for other component types.
OpenShift Origin v3 provides more application topologies. Developers can
have a project in which many components are linked with each other. In this
manner, we can link any two arbitrary components together through
exporting and consumption of environment variables. This way, we can link
together any two components without having to change the images they are
based on.
Given the above advantages, we can build anything on OpenShift by offering a platform
built on containers that allows building entire applications in a repeatable lifecycle. To
address the complexity of deploying on Open Shift Origin, Fraunhofer FOKUS has
developed and abstraction layer – NUBOMEDIA PaaS Manager on top of Open Shift
Origin that uses a web based GUI interface and REST APIs to interact with Open Shift
micro services in building, deploying, scaling and hosting NUBOMEDIA multimedia
applications.
Remember the fact that, the PaaS is somewhere in between IaaS and SaaS, thus the
NUBOMEDIA PaaS Manager also provides connection and interaction with
OpenBaton (an ETSI NFV complaint network orchestrator) for requesting and
instantiating virtual network resources (e.g. media servers)
4.3.3 NUBOMEDIA outcomes
The PaaS Manager and PaaS API extensions to the OpenShift Origin PaaS have been
implemented, tested and pushes to the fhg-fokus-nubomedia github repository
[FOKUS_GITHUB]. The associated software artifacts have ben releases as part of the
NUBOMEDIA Open Source Software Community and shall be disseminated along the
project. At the time of this writing, a scientific paper has been submitted to a relevant
journal featuring our PaaS architecture.
4.3.4 References
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Referencing websites
[5G_EX] https://5g-ppp.eu/5gex/
[5G_SONATA] http://www.sonata-nfv.eu/
[CLOUDIFY] http://getcloudify.org/
[FOUNDRY] https://www.cloudfoundry.org
[SHIFT] https://www.openshift.com
http://www.infoworld.com/article/2871935/application[INFO_WORLD]
development/infoworlds-2015-technology-of-the-year-award-winners.html
[APP_ENGINE] https://cloud.google.com/solutions/architecture/webapp
[HEROKU] https://www.heroku.com
[SOLUTIONS_REVIEW] 2016 Comparison Matrix Report - Platform as a Service
http://solutions(PaaS)
review.com/dl/2016_Solutions_Review_Buyers_Matrix_Cloud_PaaS_NYE16.pdf
[FOKUS_GITHUB] https://github.com/fhg-fokus-nubomedia
Referencing papers or books
[NIST] P. Mell and T. Grance, “The NIST Definition of Cloud Computing,” NIST
Special Publication 800-145, September 2011.
[FORRESTER_REP] S. Ried, “Multiple PaaS Flavors Hit The Enterprise,” Forrester,
Tech. Report., August 2012, http://www.forrester.com/Multiple+PaaS+Flavors+
Hit+The+Enterprise/fulltext/-/E-RES78101
[GARTNER_MODEL] Y. V. Natis, “Gartner Reference Model for PaaS” Gartner,
Tech. Rep., September 2011, http://www.gartner.com/id=1807820
4.4 Media monitoring in cloud infrastructures
Monitoring is a terminology that depends a lot on the context it refers but in the case of
computer systems is a process to of becoming aware of the current state of a computer
system. Current IT system are very complex and managing and monitor the state of
them are needed real-time systems to gather metrics, store them in a time-series
database and interpret the readings. Monitoring a system means finding out about
complications before they develop into problems and keep high SLAs. They also
provide historical data for developing predictive solutions.
In general monitoring begins with reading a specific metric, store it and measure against
thresholds. Important about the monitoring metrics is the resolution of the values they
are stored. If metrics are stored at 5 minutes intervals they will not help a developer
troubleshoot the latency on his multimedia application. Is possible that latency to
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increase and when the monitoring system is measuring the value to be decreased and in
this way deceives the developers that has the impression that latency is in parameters.
Monitoring of a computer systems involves multiple fields like storage systems, realtime processing, statistics and data analysis. A monitoring system is usually a set of
software components that collects data, process data and then presents the data to an
user. Time-series data are basically chronologically ordered lists of data points. A
metric and his properties in a data structure are stored in so called time-series databases.
Benefits of using monitoring systems are immediate, they provide early detections of
problems, helps business to make decisions. Some examples are
Netflix[INFONETFLIX] which monitors all the company cloud infrastructure and
provide open-source tools for large scale monitoring. By leveraging data from
monitoring, Netflix understands the quality of the service is provided to customers and
what aspects of the service needs improving.
Data collection in a monitoring system is responsible with gathering meaningful data
from systems by using agents. The collection is a continuously process that reads the
data at intervals that are not affecting performance of the monitored system. Then data
is stored at specific intervals, the previously mentioned resolution or granularity.
Common intervals are 1, 5, 15 and 60 minutes. Some systems have fixed granularity but
others have a hybrid approach where data points are stored for a period in a fixed
granularity but later are in a different. This hybrid approach helps saving costs for
storing the metrics.
Collecting data can be active or passive. An active monitoring is adding a cost to the
monitoring for example by doing a ping request is added a small network load. So
intervals of collection are critical to not increase the network costs. A passive
monitoring is reading statistics from current flow of data without adding any cost for
collecting the data.
Now that we have an understanding what a monitoring system is we’ll describe in next
section current monitoring technologies.
4.4.1 Description of current SoTA
This section will describe current monitoring solutions with time-series databases with
their features and characteristics that we followed when we investigated to use them for
NUBOMEDIA.
4.4.1.1 InfluxDB
Is a young open-source project that is building a time-series database which aims to
solve the problem with scalability of large number of writes from different sources.
Being built from scratch with latest technologies and specifically for time-series
databases allows InfluxDB [INFLUXDB] to achieves his goals of high scalability and
performance.
InfluxDB has powerful APIs that allows developers to push data via REST APIs and
perform queries which are expressive and SQL-like.
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Figure 26 InfluxDB design
As shown on Figure 26, the design of database is distributed and data is sharded, where
it borrowed concepts from other databases. Being a time-series database, most of the
data is sharded by timestamp.
As shown in Figure 27 InfluxDB is managed through a web interface and most of the
functions are available in the interface like creating new databases and performing
queries and displaying the stored data. Data stored on InfluxDB can be tagged so
queries can target aggregated data from multiple sources. All the data is indexed by tags
and time.
Figure 27. InfluxDB Web Interface for management
An issue with InfluxDB is that the database is distributed which makes it more complex
so inherently harder to manage it in deployment scenarios and multiple systems should
be monitored. This was the main reason why InfluxDB was not considered for
NUBOMEDIA as it added a lot of complexity and the project is quite young(latest
version is 0.9).
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4.4.1.2 Graphite
From current solutions, Graphite [GRAPHDOCS] is one of the oldest and most used
open source projects dedicated to monitoring. It was started in 2006 by an online
agency company and released as an open source project in 2008 [MONITGRAPHITE].
Having a simple interface to push metrics lots of contributors were involved in Graphite
by adding improvements to performance and addressing an use where results where
needed in real-time. This is very important for NUBOMEDIA which real-time is the
most important requirement for a multimedia application.
Many businesses use or used Graphite [MONITGRAPHITE] as their monitoring
platform to store metrics and taking business decisions on the data it collected.
Booking.com one of the Internet’s busiest online travel agency is using Graphite as their
primary system for storing performance data for systems, network and application.
Graphite enabled them to store easily data from multiple sources and correlate data
across all of them and understand the full impact of a code, infrastructure or feature
change.
Other users of Graphite are GitHub, which manage software development pipeline of
many commercial and open source projects. Their culture of data driven investigation
powered by Graphite enabled them to interact with data in a manner that was visible for
all employees of the company, from ops to marketing.
Figure 28. Graphite architecture
As shown on Figure 28, Graphite is composed from multiple components
[MONITGRAPHITE]:
• Carbon: A Python based daemon that listens to the data and is storing it on
Whisper storage. Metrics are submitted in a simple format with a name
delimited by dots, a epoch timestamp and a value. Carbon can be installed on a
different machine than rest of Graphite components so it can be used an
optimized machine for Carbon which is CPU bound, compared to Whisper
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•
•
which will require a server optimized for IO operations. Also, Carbon can
aggregate values for use cases like monitoring a server with 8 cores and some
operators will prefer an aggregated display of CPU usage.
Whisper: is a time-series database that is storing the metrics to one-second
precision. Each datapoint will be stored as float with an epoch timestamp. Is
based on RRD and improved by allowing data to be stored at different intervals
and backfilling old data. Each metric will create a file on the filesystem and an
empty Whisper database will have prepopulated all the datapoints that will
arrive for that metric with null values. These null values are replaced as data
arrives and stored by Carbon. Retention of metrics are optimized by specifying
the precision needed and period. For example 1sec metrics to be stored for 15
days, then datapoints to be roll up on at 1min interval. This is achieved by using
an aggregation method which can be defined. Default method of aggregation is
average of values. So for our example, from 60 datapoints for each second are
aggregated in a single value, an average of those 60 values. If needed
aggregation method can be sum, min, max or last.
Graphite Web application: A Django/Python application that is serving the data
from Whisper to users via a WEB interface or REST APIs. Web application is
using Memcached to optimize the performance of Graphite. Memcached is an
in-memory key-value store that stores the results form the backend.
Figure 29 displays how all Graphite components interact. Hosts and applications are
pushing metrics to Carbon via TCP sockets or REST API. The metrics are stored on
Whisper database and server to Graphite web application for display.
Figure 29. How Graphite components interact
Being a mature project and filling requirements needed for NUBOMEDIA project, we
choose Graphite as the monitoring tool for storing metrics from applications and
resource usage of servers, containers used on NUBOMEDIA platform.
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4.4.1.3 Prometheus
Started in 2012 by SoundCloud company and released as an open source project,
Prometheus [PROMETHEUS] is a monitoring system with a feature-rich time-series
database with no reliance on distributed storage.
Figure 30. Prometheus architecture
Compared to previous systems, Prometheus uses a pull model over HTTP as shown
inFigure 30 . The server is querying clients for data, in this way the server decides what
to pull and when to pull the data. For example a server under heavy load can throttle
better the network traffic as the clients will store the metrics until server is ready.
Prometheus has a rich data model (time series identified by metric name and key/value
pairs) and with an advanced query system that can filter, group data based on labels
from metadata. Prometheus query system can be accessed with a HTTP API.
Main disadvantage of Prometheus is that the events are not stored per events but they
are preprocessed on the clients. For example, on Prometheus will be stored that for 1
minute a server had 300 requests and we’ll not store each individual request. This limits
the use cases where can be used, but for NUBOMEDIA requirements this was not the
case.
The reason we didn’t used Prometheus as monitoring platform for NUBOMEDIA is
that is not supporting down-sampling and metrics storage can become costly for a large
number of servers in long term. Down-sampling on monitoring is the process of
removing entry-points at different intervals, for example if latency was stored initially at
each second, after 15 days it can be downscaled and latency to be stored at each minute.
In this way older data that is not used anymore will use less storage.
4.4.2 NUBOMEDIA approach beyond SotA
All the solutions mentioned at 3.4.1 have a granularity of data for 1, 5, 15 or 30
minutes, which is not satisfying the requirements of NUBOMEDIA where real-time
multimedia applications are deployed and require close to real-time monitoring
information.
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Considering current monitoring tools offerings and the real-time requirements of
NUBOMEDIA, we decided that Graphite and his flexible time-series database satisfies
the requirements for the project and we integrated it in the platform. Also we fine-tuned
the solution to be able to monitor metrics at a granularity that helps developers for realtime applications but also reduce the costs for storing the monitoring data.
4.4.3 NUBOMEDIA outcomes
We integrated Graphite into NUBOMEDIA platform and added on top of it an API for a
better experience for developers to submit metrics. As part of the integration we
optimized storage component to store time-series data at granularity that make the
system useful for real-time applications.
The development was integrated in NUBOMEDIA as part of deliverable D3.2.
The optimizations performed will be presented to Distributed Systems scientific
seminar from dec 2016: https://www.eed.usv.ro/SistemeDistribuite
4.4.4 References
Referencing websites
[INFONETFLIX] http://www.infoq.com/presentations/netflix-monitoring-system
[INFLUXDB] https://influxdata.com/time-series-platform/influxdb/
[GRAPHDOCS] https://graphite.readthedocs.org
[PROMETHEUS] http://prometheus.io
Referencing papers or books
[MONITGRAPHITE] J. Dixon, Monitoring with Graphite, O’Reilly Media, 2015
4.5 Deploying and installing media cloud infrastructures
OpenStack is a cloud operating system that provides support for provisioning large
networks of virtual machines, pluggable and scalable network and IP management, and
object and block storage. OpenStack is a complex system of components, each requiring
expertise to deploy and manage. The system administrators require the same agility and
productivity from their hardware infrastructure that they get from the cloud.
Infrastructure-as-Code (IaC) automates the process of configuring and setting up the
environment (e.g., servers, VMs and databases) in which a software system will be
tested and/or deployed through textual specification files in a language like Puppet or
Chef [HUMBLE2010].
Given that testing and deploying a cloud computing platform like OpenStack requires
continuous configuration and deployment of virtual machines, OpenStack makes
substantial use of IaC, adopting both Puppet [PUPPET] and Chef [CHEF] to automate
infrastructure management [JIANG2015].
More recently, OpenStack has started collaborating both with Chef [CHEF.IO] and
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functional OpenStack environments on bare-metal hardware, as well as on Vagrant
virtual machines. The combination of a system management tool, like Chef or Puppet,
and Vagrant can be used to setup a virtualized experimentation environment. Puppet
Enterprise offers solutions to deploy OpenStack and scale it horizontally on demand.
4.5.1 Description of current SoTA
The current available technologies for system administration and management are the
following:
• Chef https://www.chef.io/chef/
• Puppet Enterprise https://puppetlabs.com/puppet/puppet-enterprise
• AWS OpsWorks http://aws.amazon.com/opsworks/
• JUJU https://juju.ubuntu.com/
These tools provide domain-specific declarative languages (DSL) for writing complex
system configurations, allowing developers to specify concepts such as “what software
packages need to be installed”, “what services should be running on each hardware
node”, etc. More recently, OpenStack has started collaborating both with Chef
[CHEF.IO] and Puppet [PUPPETLABS.COM] to create new means to configure and
deploy fully-functional OpenStack environments on bare-metal hardware, as well as on
Vagrant virtual machines [AFFETTI2015]. The combination of a system management
tool, like Chef or Puppet, and Vagrant can be used to setup a virtualized
experimentation environment.
AWS OpsWorks [AWSAMAZONCOM] is an Amazon product that allows resources to
be provisioned via the EC2 service and configured using Chef. Chef recipes can be
applied at a number of defined lifecycle stages (Setup, Configure, Deploy, Undeploy,
Shutdown) [MEYER2013].
OpsWorks allows deployments to be built as stacks composed of the individual
components of the deployment, such as webservers and databases. Cookbooks can be
retrieved from SVN and GitHub repositories, HTTP addresses and S3 buckets. Recipes
can be executed directly in virtual machine instances, eliminating the need for a
dedicated Chef server. OpsWorks is a propriety hosted service and can be used only
with the Amazon EC2 cloud service.
Juju is an open-source service configuration and deployment tool developed by
Canonical [JUJU]. It is limited to deploying systems running the Ubuntu Linux
distribution. It is compatible with Linux containers, physical machines and cloud
environments, such as Amazon EC2, Eucalyptus, OpenStack, HP Cloud and
RackSpace. Juju service configuration scripts are called charms and are shared through
a public catalogue called the charm store. The charms are stored on the Ubuntu
Launchpad platform and can be written in any language that can be interpreted by
Ubuntu [MEYER2013]. This makes it possible to use it in combination with Puppet or
Chef, as Juju will just call the agent to run a script or to connect to a Puppet Master or
Chef server. Currently it is only possible to assign one charm per instance. Instance
specification parameters, such as memory, can be used as prerequisites for charms.
A number of commercial systems, such as IBM Tivoli, BMC Bladelogic Automation
Suite, HP OpenView and Microsoft Center Configuration Manager are also available
[MEYER2013]. Access to script catalogues for these systems, if they exist, is typically
restricted to paying clients. Ad-hoc sharing of scripts can also occur on forums and
mailing lists.
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These are complex sysadmin tools that require:
• strong technical skills;
• specialized personal;
• complex setups;
• aimed to large enterprises.
The currently available FOSS solutions used are:
• Puppet open source - https://puppetlabs.com/puppet/puppet-open-source
• Chef Open source - https://github.com/chef/chef
• Saltstack - http://saltstack.com
• Bash scripts https://www.gnu.org/software/bash/
• Python application https://www.python.org/
• Ruby application https://www.ruby-lang.org/en/
The two most popular open source automated configuration management (CM)
software tools all follow the declarative approach: Puppet and Chef. The scripts are
developed in a collaborative manner on social coding sites and are shared through
publicly accessible catalogues or community forums.
Puppet is an automated configuration management (CM) and service deployment
system that has been in development since 2005. Puppet configuration scripts are
referred to as manifests. It is implemented using the Ruby programming language, and
hence requires an installation of Ruby to be present on machines that it manages. For
cloud environments Puppet has a suite of command-line tools to start virtual instances
and install puppet on them without logging in manually.
Puppet Labs provide a manifest sharing platform, Puppet Forge [FORGE] that is used
by the community to promote re-use and collaborative development of manifests.
Analysis of the manifests available on Puppet Forge indicates that the user base is
focused primarily on Linux, especially Ubuntu and RHEL based systems.
The manifests available on Puppet Forge are open source and address a wide variety of
service deployment and administration tasks. The manifests themselves are written
either in a Puppet description language (a DSL using simplified Ruby), or directly in
Ruby.
Two web interfaces are available for Puppet. The first, Puppet Dashboard, is developed
by Puppet Labs and is used as the interface in the commercial version, although a
community version with a reduced set of functionality is freely available. The second
interface is Foreman, which has more detailed views and integrated support for compute
resources, such as cloud service providers. Foreman is an open source project that is
built and maintained by a community of volunteers with assistance from Red Hat.
Chef is an open source CM tool and framework developed by Opscode, a company
founded in 2008 [EWANT2013]. Furthermore, it exposes APIs and libraries that can be
used to build custom tools. The configuration scripts are called cookbooks that consist
of recipes. Chef is built on a client-server model, where the Chef client connects to the
Chef server to pull the cookbooks and to report the current state. The server is also the
reference implementation of the Chef API. The client is written in Ruby and therefore
depends on the Ruby runtime. Recipes can be implemented either in pure Ruby or a
reduced Ruby DSL.
Chef is divided into multiple components. The multi-purpose command line tool Knife
uses the Chef framework to facilitate system automation, deployment and integration. It
provides command and control capabilities for managing virtual, physical and cloud
resources.
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Open source social coding communities, such as GitHub [DABBISH2012], are often
used to share scripts for open source automated CM tools and encourage collaborative
development.
For example, the “openstack-chef-repo” repository is an example project for deploying
an OpenStack architecture using Chef, and the “puppetlabs-openstack” project is used
to deploy the Puppet Labs Reference and Testing Deployment Module for OpenStack,
as described in the project profile on GitHub [GITHUB-PUPPETLABS], [GITHUBCHEF].
These solutions have the following limitations
• no gui tools.
• no solution for configuring OpenBaton and the PaaS manager
The modular architecture of these management tools enables the FOSS community to
collaborate and share plug-ins to support any operating system and any boot sequence.
Puppet Labs and EMC collaboratively developed Razor, a next-generation physical and
virtual hardware provisioning solution [PUPPETLABS.COM]. Puppet Enterprise with
Razor automates every phase of the IT infrastructure lifecycle, from bare-metal to fully
deployed applications. Razor will automatically deploy the correct operating system or
hypervisor to the appropriate hardware by matching hardware profiles to a defined
policy. This tool will provide unique capabilities for managing hardware infrastructure,
including:
• Auto-discovered real-time inventory data for every hardware node, eliminating
inefficient, error-prone manual processes;
• Dynamic image selection which allows to removing the need for manual
intervention whenever there is a change in hardware configuration;
• Policy-based provisioning allowing specifying the desired state of each
hardware node and its operating system, automatically tracks provisioning
progress toward this state, and can even decide when to re-provision.
• RESTful open APIs and plug-in modular architecture which gives full
programmatic control of the rules and models that govern operating system
image selection and hardware provisioning.
FutureGrid [LASZEWSKI2010], now FutureSystems [FUTURESYSTEMS] is a project
funded by NSF, USA that set out to develop an environment that researchers could use
to experiment with cloud infrastructures and various kinds of HPC platform services
(e.g., Hadoop). Initially, FutureGrid supported IaaS deployments through Nimbus,
OpenStack, and Nebula and now the FutureSystems supports IaaS deployments only
through OpenStack. FutureSystems is a well-known project allowing fast access to a
functional cloud installation, focused to experiment the potential behavior of a specific
application across different cloud providers.
OpenStack APIs has multiple SDKs for Ruby but none of them is a native one, making
it not the best solution because of the capabilities that might not be implemented on the
third party SKDs.
Using the OpenStack Python native APIs we can develop the NUBOMEDIA platform
autonomous installer having access to all the capabilities the IaaS can provide in an easy
and direct manner.
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4.5.2 NUBOMEDIA approach beyond SoTA
For adapting to the specific requirements of NUBOMEDIA, we created a free open
source autonomous installer based on Python that allows the deployment of the
NUBOMEIDA platform on top of an IaaS based on OpenStack and a PaaS based on
OpenShift.
4.5.3 NUBOMEDIA outcomes
We published the autonomous installer [AUTONOMOUS-INSTALLER] on a public
github repository20 so that anyone who wants to use NUBOMEDIA on it’s private data
center to be able deploy it in just few minutes without having to run installation and
configuration scripts. The results of task T3.5.2 will be disseminated by publishing a
research paper at the DAS conference 2016 [DAS conference 2016].
4.5.4 References
Referencing websites
[FUTURESYSTEMS] https://portal.futuresystems.org/ ;
[CHEF] “Chef Software, Chef - IT Automation for Speed and Awesomeness”,
https://www.chef.io/chef/, 2015 ;
[PUPPET] “Puppet Labs, Puppet - IT Automation Software,” http://puppetlabs.com/.
[CHEF.IO] https://www.chef.io/solutions/openstack/ ;
[PUPPETLABS.COM] https://puppetlabs.com/solutions/openstack/ ;
[GITHUB-PUPPETLABS] github.com/puppetlabs/puppetlabs-openstack ;
[GITHUB-CHEF] github.com/stackforge/openstack-chef-repo ;
[FORGE] forge.puppetlabs.com/ ;
[AWSAMAZONCOM] “AWS OpsWorks DevOps cloud application management
solution”, aws.amazon.com/opsworks/ ;
[JUJU] juju.ubuntu.com/ ;
[AUTONOMOUS-INSTALLER] https://github.com/usv-public/nubomediaautonomous-installer .
[DAS conference 2016] http://www.dasconference.ro/ ;
Referencing papers or books
[EWANT2013] Ewart, J. , “Instant Chef starter”, Birmingham: Packt, (2013).
[DABBISH2012] Dabbish, L.; Stuart, C.; Tsay, J. and Herbsleb, J.,“Social coding in
GitHub: transparency and collaboration in an open software repository”, ACM 2012
Conference on Computer Supported Cooperative Work (CSCW ’12), New York, NY,
USA: ACM, (2012):1277–1286.
[JIANG2015] Jiang, Yujuan; Adams, Bram, “Co-evolution of Infrastructure and Source
Code - An Empirical Study”, IEEE 12th Working Conference on Mining Software
Repositories, (2015): 45-55.
[HUMBLE2010] Humble, J. and Farley, D., “Continuous Delivery: Reliable Software
Releases Through Build, Test, and Deployment Automation”, 1st ed. Addison-Wesley
Professional, 2010.
[MEYER2013] Meyer, Stefan; Healy, Philip; Lynn, Theo; Morrison, John, “Quality
Assurance for Open Source Software Configuration Management”, 15th International
Symposium on Symbolic and Numeric Algorithms for Scientific Computing, (2013):
454-461.
20
https://github.com/usv-public/nubomedia-autonomous-installer
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[LOPEZ2014] Lopez, Luis, et al. "Authentication, Authorization, and Accounting in
WebRTC PaaS Infrastructures: The Case of Kurento." Internet Computing, IEEE 18.6
(2014): 34-40.
[AFFETTI2015] Affetti, L., Bresciani, G., and Guinea, S., “aDock: A Cloud
Infrastructure Experimentation Environment based on OpenStack and Docker”, IEEE
8th International Conference on Cloud Computing (CLOUD 2015), June 27 - July 2,
2015, NY, USA, (2015): 203-210.
[LASZEWSKI2010] Laszewski, G. Von; Fox, G. C.; Wang, F.; Younge, A. J.;
Kulshrestha, A.; Pike, G. G.; Smith, W.; Voeckler, J.; Figueiredo, R. J.; Fortes J. et al.,
“Design of the Futuregrid Experiment Management Framework,” Gateway Computing
Environments Workshop, (2010): 1–10.
5 RTC media server technologies
5.1 RTC media servers
5.1.1 RTC media servers: an overview
In the context of RTC systems, a media server is a software stack (i.e. a subsystem) that
provides media capabilities. Media capabilities are all the features related to the media
itself (i.e. related with the bits of information representing audio and video). These may
include:
• Media transport following specific RTC protocols
• Media recording and recovery
• Media transcoding
• Media distribution
• Media analysis
• Media augmentation and transformation
• Media mixing
• Etc.
In the RTC area, sometimes media servers are frequently named depending on the
specific capabilities they provide. For example, we speak about streaming media servers
when dealing with media servers capable of streaming multimedia content, or we say
recording media server when we have the capability of recording the audio and video
received, etc.
The concept of media server is quite old and has been used in the RTC literature for
long time. In the last few years, RTC media servers are living again a new gold era due
to the emergence of WebRTC technologies. Nowadays, WebRTC is the main trending
topic in the multimedia RTC area and this is why WebRTC needs to be supported by
any RTC media server wishing to play a role in the market. WebRTC services
commonly require the presence of media servers, which are very useful when creating
services beyond the standard WebRTC peer-to-peer call model. Some common
examples of server-side media plane elements that could be used in WebRTC include:
• WebRTC media gateways – typical on services requiring protocol or format
adaptations (as happens when integrating WebRTC with IMS)
• Multi Point Control Units (MCUs) and Selective Forwarding Units (SFUs) –
used to support group communications
• Recording media servers – helpful when one needs to persist a WebRTC call
• Etc.
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Figure 31. RTC applications, in general, and WebRTC applications, in particular, may use two different models. As
shown at the top, the peer-to-peer model is based on direct communication among clients. This model provides
minimum complexity and latency, but it also has important limitations. At the bottom, the infrastructuremediated model, where a media server is mediating among the communicating clients. This model has higher
latency and complexity, but it makes possible to enrich RTC services with additional capabilities such as
transcoding (i.e. interoperability), efficient group communications (i.e. MCU or SFU models), recoding and media
processing.
Currently, there are a large number of WebRTC capable media servers out there. Just
for illustration, Table 6 shows the list of the me most popular open source ones.
Server
Medooze
Description
21
Licode 22
Jitsi 23
Intel CS for WebRTC
Client SDK 24
telepresence 25
Janus 26
Meedoze it is a MCU Video Multiconference Server with WebRTC support.
It can be integrable in any SIP infrastructure. It provides web streaming and
recording of conferences supported, custom layouts and continous presence,
and web management interface.
The Licode media server provides MCU videoconference rooms and
recording features totally compatible with the WebRTC standard.
Jitsi Videobridge is a WebRTC compatible Selective Forwarding Unit (SFU)
that allows for multiuser video communication. Unlike expensive dedicated
hardware videobridges, Jitsi Videobridge does not mix the video channels
into a composite video stream. It only relays the received video flows to all
call participants.
The Intel CS for WebRTC Client SDK builds on top of the W3C standard
WebRTC APIs to accelerate development of real-time communications
(RTC), including broadcast, peer-to-peer communications, and conference
modes.
The open source media server of the Doubango initiative which provides
group communications through a MCU mixing model and some additional
features which include recording, 3D sound, etc.
Janus is a WebRTC Gateway developed by Meetecho conceived to be a
general purpose one. As such, it doesn't provide any functionality per se
other than implementing the means to set up a WebRTC media
communication with a browser, exchanging JSON messages with it, and
21
http://www.medooze.com/
http://lynckia.com/licode/
23
https://jitsi.org/
24
https://software.intel.com/en-us/webrtc-sdk5
25
https://code.google.com/p/telepresence/
26
https://janus.conf.meetecho.com/
22
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Red5 27
relaying RTP/RTCP and messages between browsers and the server-side
application logic they're attached to. Any specific feature/application is
provided by server side plugins that browsers can then contact via the
gateway to take advantage of the functionality they provide. Example of
such plugins can be implementations of applications like echo tests,
conference bridges, media recorders, SIP gateways and the like.
Red5 is an open source media server for live streaming solutions of all kinds.
It is designed to be flexible with a simple plugin architecture that allows for
customization of virtually any VOD and live streaming scenario. Built on the
open source Red5 Server, Red5 Pro 28 allows you to build scalable live
streaming and second screen applications.
Table 6. Open source software WebRTC media servers.
In the proprietary arena, there are currently many vendors offering different types of
WebRTC Media Servers providing the above mentioned features, a non-exhaustive list
containing some of them (in no particular order) is the following:
• The Dialogic Power Media 29 product line offers different types of WebRTC
capable media servers providing all the common media server capabilities (i.e.
transcoding, MCU, recording, etc.)
• Some of the Radisys MRF 30 products claim to offer common media server
capabilities enabled for WebRTC endpoints.
• Oracle, rebranding the acquired Acme Packet media server product line, is also
offering a product line 31 containing different types of WebRTC middleboxes
including media servers and session border controllers.
• Italtel seems also to have adapted its media server 32 product line for providing
WebRTC capabilities.
• Flahsphoner also provides now a WebRTC capable media server 33 for group
communications.
• The NG Media media server seems also to have introduces WebRTC 34 endpoint
capabilities.
• Ericsson offers through its Web Communication Gateway 35 different WebRTC
media server capabilities.
27
http://red5.org/
https://red5pro.com/
29
http://www.dialogic.com/en/landing/webrtc.aspx
30
http://www.radisys.com/products/mrf/mpx-12000/
31
http://www.oracle.com/us/industries/communications/oracle-enterprise-web-rtc-wp-2132263.pdf
32
http://www.italtel.com/en/media-center-eng/press-room/item/italtel-launches-embrace-webrtc-solution
33
http://flashphoner.com/webrtc-streaming-server-for-live-broadcasting-and-webinars/
34
http://n-g-media.com/ng-media-reveals-the-support-of-webrtc/?lang=en
35
http://www.ericsson.com/us/ourportfolio/products/web-communication-gateway
28
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Figure 32: Media capabilities provided by state-of-the-art media server include: transcoding (top), group
communications (middle) and archiving (bottom).
However, as NUBOMEDIA’s vision shows, there are many interesting things we can
do with the media beyond the basic three capabilities enumerated above (i.e.
transcoding, group communications, recording). Why not enrich it using augmented
reality? Why no analyze it using computer vision or deep speech analysis? Why can’t
we blend, replicate or add special effects to the media as it is travelling? These kinds of
capabilities might provide differentiation and added value to applications in many
specific verticals including e-Health, e-Learning, security, entertainment, games,
advertising or CRMs just to cite a few.
Due to this, NUBOMEDIA requirements cannot be satisfied with any of the above
mentioned solutions. Hence, for the execution of the project we need a more flexible
technology where advanced processing capabilities could be plugged seamlessly. For
this, we have re-architected the only open source media server enabling such types of
capabilities: Kurento Media Server (KMS).
KMS vision is based on transforming traditional media server technologies, which are
tied to a set of specific features, into modular software having the ability of extending
the provided features just adding further plug-ins. In this direction, KMS is just a
container providing a number of basic building blocks for implementing low level
operations that provide plumbing capabilities enabling to move media from one place to
another. Modules are just specialized classes that are capable of receiving and sending
media to other modules through that plumbing.
Due to this vision, KMS is a flexible technology capable of providing all types of
features one can image for the media, which is a clear advantage and justification for
NUBOMEDIA choosing it. However, KMS also has drawbacks. The first one is paid in
terms of complexity. For example, the software complexity, in terms of source code
lines, classes, files, dependencies or any other metric you may wish to use, of KMS is
significantly higher to the ones shown by the rest of open source media server
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technologies described above. The second one is on performance. The required degree
of flexibility makes the architecture to require on all modules the provision of
capabilities that may be memory and CPU consuming, but which are not always used
on a given specific application.
5.1.2 Description of current scientific and engineering SoTA
There is not a formal definition of what a media server is and different authors use the
term with different meanings. In this document, we understand that a media server is
just the server side of a client-server architected media system. In the same way DD.BB.
(Data Base) servers provide data persistence capabilities to WWW applications, media
servers provide multimedia capabilities to media-enabled applications.
Following this definition, media server technologies emerged in the 90’s catalyzed by
the popularization of digital video services. Most of these early media servers were
specifically conceived for providing multimedia content distribution and transport in
two main flavors: streaming media servers and RTC (Real-Time Communication)
media servers.
Streaming media servers [DASHTI2003, LAURSEN1994, LI2013] provide media
distribution capabilities through transport protocols designed for reliability that tend to
relax latency requirements [SEUFERT2014]. This means, among other things, that
these protocols typically recover from packet loss in the network through iterative
retransmissions. The QoS provided through them is convenient for one-way
applications such as VoD (Video on Demand) or live streaming, where the media
information travels from sources to destinations but there is no real-time feedback
communication.
RTC media servers [SCHULZRINNE1999, LU2010] in turn, are designed for
bidirectional communications. Due to this, the transport protocols they implement are
designed for low latency and packet retransmissions are not always useful for
recovering form losses. In these services, the full duplex media information exchange is
used to provide conversational interactions among users. Due to this, this type of media
servers are typical in audio and video conferencing systems. In this document, we
concentrate our attention on this latter type of media servers.
During the last two decades, RTC media servers evolved through different types of
standards. One of the most remarkable ones is H.323[THOM1996], where the media
server is performed by an architectural module called MCU (Multipoint Control Unit).
The IMS (IP Multimedia Subsystem) architecture also standardized a generic media
server function as the MRF (Media Resource Function) [KOUKOULIDIS2006]. Other
standardization bodies worked in the same direction: the IETF (Internet Engineering
Task Force), with specifications such as MGCP, MSCML, MSML or the JCP (Java
Community Process) with API standards such as the Jain MEGACO API or the Media
Server Control API. All is can be observed, all these efforts were concentrated on the
standardization of the media server control interfaces more than on generating
architectural recommendations or implementation guidelines
Most of these standards were issued by telecommunication operators and vendors to
solve their very own needs. Due to this, RTC media servers remained as niche
technologies used only on those areas. However, in the last few years, the emergence of
WebRTC [LORETO2012] is bringing RTC services to mainstream WWW and mobile
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developers worldwide. WebRTC enables high quality real-time multimedia
communications on the Internet in a standardized, universal and interoperable way.
WebRTC makes it possible for developers to incorporate RTC as an additional feature
of WWW and mobile applications in a seamless way. As a result, WebRTC is
permeating into many application domains including e-learning, e-health, CRM
(Customer Relationship Management), gaming, robotics, IoT or video surveillance, just
to cite a few.
Plain client WebRTC implementations just enable peer-to-peer communications.
However, in the last few years users demand for richer services is exploding. This is
making (Web)RTC media servers to become a critical asset for the success of
applications. In particular, most available RTC media servers are designed and
optimized for providing one of the following capabilities:
• Group communication capabilities: These refer to the ability of enabling groups
of users to communicate synchronously. Most group videoconferencing services
require them.
• Media archiving capabilities: These are related to the ability of recording
multimedia streams into media repositories and of recovering them later for
visualization. Services requiring communication auditability or persistence use
them.
• Media bridging: This refers to providing media interoperability among different
network domains having incompatible media formats or protocols. These are
used, for example in WebRTC gateways, which interconnect WebRTC browsers
with and legacy VoIP systems, and on IMS architectures.
Following this, in this document we introduce a next generation (Web)RTC media
server that contributes to the state of-the-art by following a holistic architectural
approach, meaning that it has been designed for providing, in an integrated way, all
types of server-side media capabilities. This includes the three specified above but also
further ones, such as augmented reality, media content analysis or arbitrary media
processing; which enable novel use cases for rich person-to-person, person-to-machine
and machine-to-machine communications. The next sections are devoted to specifying
how Kurento contributes to these areas in detail.
5.1.2.1 Modularity in media server architectures
In software development, the concept of module is typically used for referring to a
specific functional block that is hidden behind an interface. Following this definition,
many modern RTC media servers are modular [AMIRANTE2014]. However, beyond
the intuitive idea of a module, we are interested in the stronger concept of modularity
[BALDWIN2001].
From a developer’s perspective, an RTC media server is just a framework (i.e. a system
providing capabilities to a system designer for programming application on top.) When
bringing the concept of modularity to frameworks, the main idea is to split systems into
smaller parts (i.e. modules) so that there is strong cohesion within modules and loose
coupling among them. Moreover, for having full modularity they should comply with
the following additional properties:
• Isolation: when modules operate together in a system, the internal status of a
module should not directly affect the status of others.
• Abstraction: the internal state of a module should be hidden for both the system
designer and for other modules. In software, abstraction is typically achieved
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•
•
•
through interfaces that limit how the module interacts with the external world
through a limited set of primitives.
Composability: those abstract interfaces should enable components to recombine
and to reassemble in various combinations to satisfy specific users requirements.
In other words, modules should behave as building blocks and the system
designer’s role is to create the appropriate interconnection topology among them
to provide the desired logic.
Reusability: if a module provides a specific capability, any system requiring
such capability might reuse the module without the need of re-implementing
again the capability.
Extensibility: the platform should provide seamless mechanisms for creating and
plugging additional modules extending the exposed capabilities.
Modularity brings relevant benefits for developers including cost reduction, shorter
learning times, higher flexibility, augmentation (i.e. adding a new solution by merely
plugging a new module), etc. On the other hand, a downside to modularity is that low
quality modular systems are not optimized for performance. This is usually due to the
cost of putting up interfaces between modules.
In this context, and to the best of our knowledge, there is a single scientific reference
dealing with modular RTC media servers: the Janus WebRTC Gateway
[AMIRANTE2014]. Being Janus indeed modular, it does not comply with all
modularity requirements, in particular in what refers to composability (i.e. Janus
modules provide specific application logic and cannot be assembled among each other)
and reusability (i.e. in Janus, different modules need to provide similar capabilities due
to the lack of composability).
5.1.2.2 Media servers for group communications
Since their early origins, group communications were one of the most popular
applications of videoconferencing services [ELLIS1991]. Due to this, today many RTC
media servers concentrate on providing such capability [AMIRANTE2014,
GROZEV2015, NG2014]. In current state-of-the-art, there are two widely accepted
strategies for implementing group communications on RTC media servers
[WERTERLUND2016]: media mixing and media forwarding.
Media mixing is performed through the Media Mixing Mixer (MMM) Topology, in
which multiple input media streams are decoded, aggregated into a single output stream
and re-encoded. For audio, mixing usually takes place through linear addition. For
video, the typical approach is to perform video downscaling plus composite aggregation
to generate a single outgoing video grid. Hence, a participant in a group RTC session
based on MMM sends one media stream (its own audio and/or video) and receives one
media stream (the mixed audio and/or video of all participants). As MMM were the
common topology on H.323, MMM are sometimes informally called MCUs in the
literature. MMM are useful when client devices have bandwidth or CPU restrictions.
However MMM have several drawbacks. The first is that they implement CPU
intensive operations making difficult to media server infrastructures to scale. The
second is that, due also to these operations, they may increase the infrastructure latency
degrading he overall QoE (Quality of Experience).
Media forwarding is typically performed through a Selective Forwarding Middlebox
topology. RTC media servers implementing this topology are sometimes called
Selective Forwarding Units (SFU) in the literature. An SFUs clones and forwards (i.e.
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routes) the received encoded media stream onto many outgoing media streams. This
means that a participant in a group RTC session based on SFUs sends one media stream
(its own audio and/or video) and receives N-1 media streams (the audio and/or video of
the rest), being N the total number of participants. For the sake of simplicity, RTC
media servers implementing the Media Switching Mixer (MSM) topology might also be
called SFUs. SFUs and MSM share most of its properties being the only difference that
each SFU outgoing stream is uniquely mapped to one of the incoming streams. In the
RTP jargon, this means that an SFU just projects to each of its outputs a given input
SSRC and the only allowed operation is to switch on or off that projection. However,
MSMs may change the mapping of the outgoing SSRCs among different incoming
SSRCs through a switching procedure. This makes MSM to be more complex to
implement, but in exchange, they provide bigger flexibility for implementing features
such as Last N/dominant speaker or simulcast without a significant performance
decrease.
In current state-of-the-art, RTC most media servers just implement one of the above
mentioned topologies [AMIRANTE2014, GROZEV2015, NG2014] and do not provide
flexible mechanism for using the rest. Some solutions [AMIRANTE2014] enable the
ability to use different topologies as pluggable modules, so that, different applications
may use different topologies by consuming different module capabilities.
5.1.2.3 Transparent media interoperability
Transcoding media servers exist since long time ago due to the need of achieving
interoperability among incompatible media systems [AMIR1998, AHMAD2005].
Different media systems tend to have different requirements and, due to this, specific
formats and codecs were conceived during the years for satisfying them. In addition, the
existence of different commercial interests in the multimedia arena contributed to the
emergence of a multiplicity of incompatible codecs [SINGH2014]. As a result,
transcoding RTC media servers have been traditionally necessary as soon as a service
requires interoperability among different communication domains.
This situation has become even more complex with the arrival of WebRTC given that
developers typically need to interoperate WebRTC services with legacy IP
infrastructures and with the phone system [BERTIN2013, AMIRANTE2013]. Due to
this, many RTC media servers provide transcoding capabilities. However, for managing
them and create interoperable services, developers need to explicitly manage format and
codec conversions. This is in general a very cumbersome process which is error prone
and requires deep knowledge about low level details of media representation.
5.1.2.4 Systems and tools for advanced media processing
Multimedia processing technologies have been used in RTC during the last 20 years for
applications such as media compression, speech recognition, or DTMF (Dual Tone
Multi Frequency) detection [COX1998]. However, in the last few years, the spectrum of
media processing capabilities has been enlarged thanks to the emergence of novel
techniques, which include Computer Vision (CV), Augmented Reality (AR) or
Computer Generated Imagery (CGI). The volume of scientific publications in those
areas is currently overwhelming with novel tools and algorithms emerging almost daily.
However, most of these results do not arrive to be used in real applications. Probably,
the main reason for this is complexity. Leveraging most of these results typically
require deep understanding on low level media details and there is a lack of tools and
architectures enabling their seamless integration into the frameworks used by common
developers for creating applications.
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In the area of CV, for example, there are relevant initiatives for creating software tools
and frameworks suitable for simplifying the development and integration efforts of CV
technologies. These include libraries such as OpenCV [PULLI2012] , VLFeat
[VEDALDI2010] or SimpleCV [DEMAAGD2012]. All these, simplify significantly the
task of managing CV algorithms as software artifacts. However, at the time of
integrating CV mechanisms into RTC applications, and very particularly into WebRTC
applications, they do not provide the appropriate level of abstraction and the appropriate
capabilities. As a result, the number of details and complexities to manage by
developers is so high that using CV technologies in RTC applications is impractical for
most typical scenarios. However, many previous works demonstrate that enabling a
seamless convergence of RTC and CV might open new horizons in many application
domains including video surveillance [LIMNA2014] or video sensor networks
[FIDALEO2004]
When considering AR technologies [CARMIGNIANI2011] the situation is not so
different. There is a plethora of technologies, libraries and tools for creating AR
applications [AJANKI2011, OLSSON2011, MACINTYRE2011]. However, in most of
them, AR is an isolated experience that happen on the user’s. As a result, AR
technologies are rarely integrated into RTC services.
5.1.2.5 Generalized multimedia: media beyond audio and video
Traditionally, multimedia communication services have been based on the transmission
of audiovisual information. However, in the last few years, there is an increasing need
for generalizing this notion toward a multisensory multimedia model. Multisensory
multimedia typically involves several human senses beyond sight and hearing.
Multisensory multimedia may also leverage multiple types of sensors beyond cameras
and microphones. Multisensory multimedia streams are currently being used in different
application domains such as haptic RTC interfaces [ZHANG2014] virtual reality
interfaces [SUTCLIFFE2003] or entertainment [ZHOU2004].
Due to this, novel RTC media standards are providing mechanisms for low-latency
transmission of arbitrary sensor data. In particular, the WebRTC standardization bodies
have defined the DataChannel protocols and interfaces for such purpose
[LORETO2012]. DataChannels are currently being used as a transport mechanism for
peer-to-peer content distributions [NURMINEN2013] and for low-latency transport of
chat text messages. However, there are very few initiatives implementing multisensory
services leveraging DataChannels support and even less enabling full DataChannel
support in media infrastructures.
5.1.2.6 Developer tools
The objective of RTC media servers is to make possible for developers to create
applications leveraging media capabilities. Due to this, RTC media servers expose
interfaces typically in the form of APIs. Indeed, APIs are the mechanism preferred by
developers for assembling different types of capabilities into applications. However,
programming with APIs is just a part of what developers need to do for creating
applications. Many studies show that a relevant fraction of the development effort is
devoted to diagnosing problems and fixing software bugs [MEYER2014,
MAALEJ2009, ROEHM2012]. A critical ingredient for simplifying these efforts is the
provision of just-in-time visual information of software status at runtime. Due to this,
most development frameworks and IDEs (Integrated Development Environment) are
shipped with runtime debugging tools. However, in the case of RTC media servers, the
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distributed nature of applications joined to the inherent real-time nature of the software
logic, makes most of these tools useless. Moreover, from the perspective of RTC media
server vendor, such runtime information is not typically considered as relevant, reason
why most available solutions simply disregard this problem.
Transport
WebRTC
RTP
RTSP
HTTP
Archiving
File Recording
HTTP Recording
File playing
HTTP playing
Play Seek
Media Interoperability
Transcoding
Agnostic
Architecture
Modular
Composable
RTP Topologies
SFU
MSM
MMM
Media Processing
CV/VCA
AR
Alpha mixing
Multisensory
DataChannels
Synchronized metadata
Development Tools
Instrumentation
Inspector
Cloudification
QoS metrics
Modular config
Development APIs
Media Server API
XMPP
SIP
Jitsi
Licode
Telepresence
Janus
Medooze
Red5
Kurento
Yes
No
No
No
Yes
No
No
No
Yes
No
No
No
Yes
Yes(P)
No
No
Yes
Yes
No
No
No
No
No
No
Yes
Yes
Yes
Yes
Yes(P)
No
Yes(P)
No
No
Yes
No
Yes
No
No
NA
NA
NA
NA
No
Yes(P)
No
Yes(P)
No
No
Yes
NA
Yes
No
No
NA
No
NA
No
Yes
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
Yes(P)
No
Yes
No
No
No
Yes
Yes
No
No
No
No
No
No
Yes
No
No
No
No
No
Yes
Yes
Yes
No
No
Yes
No
No
Yes
No
No
Yes
No
Yes(P)
No
No
Yes
No
No
No
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
Yes
Yes
No
Yes
No
NA
No
Yes
No
NA
No
No
No
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
Yes
Yes
No
No
No
No
No
No
NA
NA
No
No
No
No
Yes
Yes
No
No
No
No
No
No
NA
NA
No
Yes(P)
Yes
No
No
No
No
No
Yes
No
No
No
Table 7. This table compares the features of the NUBOMEDIA media server (aka Kurento) with some of the most
popular open source solutions in the area of real-time-media infrastructures. Cells marked as “Yes” indicates that
the feature is supported off-the-shelf. “Yes(P)” indicates that the feature requires an external plug-in or
capability. “No” means that the feature is not supported. “NA” indicates that we have not found the appropriate
information for evaluating whether the feature is supported. This table is a best effort performed basing on
incomplete documentation provided by the cited projects and may contain mistakes.
5.1.2.7 Adaptation to cloud environments
The cloudification of RTC services is currently a very hot topic due to the benefits it
brings to network operators and service providers. Most telcos are already working on
the direction of IMS virtualization for providing novel scalable solutions of signaling
functions [CARELLA2014]. This trend is also permeating into WebRTC services,
where PaaS (Platform as a Service) clouds are becoming one of the main exploitation
mechanisms [LOPEZ2014, RODRIGEZ2016]. Current state-of-the-art in cloud
computing point to a full automation of the runtime tasks through capabilities such as
autoscaling, deployment and provisioning and service orchestration.
Due to this, RTC media servers need to adapt for complying with the requirements that
these novel clouds technologies demand. In particular, for successful cloud deployment
the following features seem to be necessary. First, to provide monitoring capabilities
enabling the management of autoscaling algorithms. Second, to incorporate the
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appropriate logic for brokering media server capabilities among applications in a
seamless and robust way. Third, to offer suitable mechanism for automating he
deployment and provisioning of media server instances. Fourth, to hold the appropriate
traffic assessment and control mechanism guaranteeing the appropriate QoS adaptation
to virtual networking environments. To the best of our knowledge, no current state-ofthe-art media servers provide all these capabilities.
5.1.3 NUBOMEDIA approach beyond SotA
In relation to the above-specified topics shortcomings and limitations, the
NUBOMEDIA approach is to re-architecture KMS technology for creating a
NUBOMEDIA Media Server enabling the following progresses beyond SotA.
5.1.3.1 Modularity in media server architectures
One of the most relevant contributions of NUBOMEDIA is that in the project we have
designed and implemented a full modular architecture complying with all the abovementioned modularity requirements. In particular, to the best of our knowledge, our
media server is the only one enabling full composability and reusability of components
thanks to its unique design based on the notion of Media Elements and Media Pipelines.
5.1.3.2 Media servers for group communications
In this area, the contributions of NUBONEDIA are straightforward: following an
holistic approach, our media server offers all topologies in an integrated way. This
enables a novel feature that we could define as “topology as an API”, in the sense that
application developers do not need to be aware of the complexities or internal details of
MMM, MSM or SFUs. They just need to use the media server API modularity features
to interconnect the appropriate building blocks in the appropriate way for satisfying
application requirements. The media server takes care of translating the API calls into
the corresponding topologies combinations and of implementing the required
optimizations and media adaptations enabling the appropriate mechanism (i.e. MMMs,
MSMs or SFUs.) to be used at the right places.
5.1.3.3 Transparent media interoperability
A very relevant contribution of NUBOMEDIA is to introduce a novel capability, that
we call the agnostic media, which performs fully transparent transcoding and format
adaptations for developers over a wide variety of codecs. To understand how this
happens, observe that the modularity requirements specified above mandate our
modules to be composable. This means that application developers should be able to
freely interconnect them for generating the desired media processing logic. From a
practical perspective this means that, for example, a WebRTC endpoint module
receiving video encoded with the VP8 codec can be connected with an RTP endpoint
sending H.264 encoded video. Of course, this type of module pipelining requires the
appropriate transcodings which, in our case, take place without even requiring
developers to know they are happening: the agnostic media detects the need of a
transcoding and performs it transparently in the most optimal possible way.
5.1.3.4 Systems and tools for advanced media processing
In this context, the main contribution of NUBOMEDIA is to provide a seamless
mechanism an a technological stack enabling the integration of advanced media
processing capabilities, and very particularly CV and AR technologies, as modular
components into a RTC media server. This mechanism has two advantages. The first is
that the abstraction properties of our architecture are suitable for hiding all the complex
details of such technologies enabling non-expert application developers to control them
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through simple and intuitive interfaces. The second is that the composability of our
APIs makes possible to assemble such advanced capabilities among each other and with
other RTC features making possible to generate innumerable combinations of rich
media processing topologies reusing the implemented algorithms and mechanism as
basic building blocks for system creation.
5.1.3.5 Generalized multimedia: media beyond audio and video
The main contribution of NUBOMEDIA in this area is the integration of WebRTC
DataChannels as a first citizen of a RTC media server. This has two implications. First,
that our WebRTC implementation fully supports the DataChannel transport and
negotiation mechanisms, for which we have been one of the main contributions to the
corresponding GStreamer repositories. Second that our modular architecture enables all
modules to manage three types of information: video, audio and data. Hence, module
implementations may leverage arbitrary sensor data received from the external word for
enhancing media processing or may generate arbitrary data streams from media
semantics. This opens novel possibilities for application developers such creating
multisensory Augmented Reality services where the augmentation logic is controlled by
external sensors, or implementing mechanisms translating audiovisual streams into
machine-understandable streams exposing rich semantic information about the media
content.
5.1.3.6 Developer tools
Another relevant contribution of NUBOMEDIA is to introduce a number of
mechanisms and APIs enabling the inspection of the runtime behavior of the RTC
media server. In particular, we have created a visual debug tool capable of depicting
graphs representing the modules involved in an application, their internal state and their
interaction topology. This tool has the objective of minimizing the effort required by
developers for understanding application runtime behavior and for gathering the
appropriate diagnose information in case of problems.
5.1.3.7 Adaptation to cloud environments
The NUBOMEDIA WebRTC media server has been designed to comply with all the
above-specified requirements for adapting to cloud environments. In particular, the
following specific capabilities have been enabled. First, we have created a metric
publishing system enabling custom QoS metrics to be gathered through a coherent API
basing on W3C WebRTC Stats API specification draft. Second, the media server
includes a Media Resource Broker function (MRB) that performs load balancing and
pipeline scheduling among media server groups basing on operating system and internal
QoS metrics. Third, we have created a modular configuration mechanism enabling
modules to expose all their parameterizable properties through them, so that the cloud
provisioning mechanism may modify and adapt them to the specific needs of each
deployment. Fourth, the media server provides a traffic shaping mechanism suitable for
avoiding overloading the virtual network interfaces of cloud environments.
5.1.4 NUBOMEDIA outcomes
The main outcome of NUBOMEDIA in the area of RTC media servers has been to
create a full re-architected version of Kurento Media Server (KMS). For this version we
have successfully implemented a fully compliant WebRTC protocol stack basing on
GStreamer capabilities. In addition, we have created the appropriate artifacts for
complying with the specific requirements of the project and for addressing all the above
mentioned progresses beyond SotA. In particular, at the time of this writing, the
following objectives have been successfully achieved:
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Modularity in media server architectures
The modularity properties of the media server have been fully implemented. Media
Element and Media Pipeline mechanism have been defined and specified through
interfaces, as described in the corresponding deliverable devoted to the NUBOMEDIA
Media Server.
Media servers for group communications
At the time of this writing, KMS provides MSM capabilities in a natural way just
through Media Element connectivity interfaces. This mechanism makes possible to
generate arbitrary and dynamic topologies where the any incoming SSRC can be
forwarded to any outgoing SSRC seamlessly and freely by application developers in
correspondence with their application logic. In addition, MMM are also naturally
provided through the Composite hub, as described in the NUBOMEDIA Media Server
deliverable document. At the time of this writing, an SFU capability providing
simulcast support has been also developed and is made available through a Media
Element interface. This capability is currently under incubation for improving its
performance and stability.
Transparent media interoperability
At the time of this writing, KMS provides a fully functional agnostic media capability
suitable for adapting media codecs and formats in a transparent way, as described in the
NUBOMEDIA Media Server deliverable document.
Systems and tools for advanced media processing
The modular mechanism enabling the integration of CV and AR capabilities as modules
of KMS has been fully developed and validated.
Generalized multimedia: media beyond audio and video
The integration of DataChannel support into the KMS WebRtcEndpoint is fully
functional and validated. The support on KMS for data tracks on Media Element
interfaces is fully functional and validated. The support for synchronous metadata
buffers is also fully functional.
Developer tools
In the context of the NUBOMEDIA project, we have created an instrumentation
mechanism enabling to access KMS runtime information. This mechanism is based on
the ServerManager interface, which exposes an API suitable for obtaining the list of
media capabilities (i.e. Media Elements and Media Pipelines) that are in place at a given
time. This API also provides subscription primitives to be notified when runtime
changes take place. In addition, runtime QoS metrics are exposed my Media Elements
through different types of interfaces, as specified in the NUBOMEDIA Media Server
deliverable.
Adaptation to cloud environments
As part of the results of this project, we have created several mechanisms for adapting
KMS to automated cloud environments. At the time of this writing, many KMS
capabilities, including the WebRtcEndpoint, provide custom QoS metrics suitable for
evaluating in a precise way the load status of the server. In addition, the modular
configuration mechanism has been fully developed and validated enabling cloud
provisioning mechanisms to automate fine grained parameterization of KMS execution.
In addition to this, the following results are expected for the last year of the project:
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Media server for group communications
We plan to evolve our current SFU architecture towards a full featured and validated
SFU supporting differentiated quality through simulcast and scalable video codec
mechanisms supporting stream multiplexing based on unified plan.
Developer tools
We plan to create a visual graphical user interface consuming KMS instrumentation
APIs for representing runtime information in a seamless and intuitive way.
Adaptation to cloud environments
We plan to perform relevant research on the definition and gathering of metrics suitable
for improving autoscaling automation mechanism. This may include the creation of
predictive algorithms suitable for detecting media server overload before it happens,
both for network and CPU.
All in all, these results have been useful for generating the following outcomes.
• Creation of a strong and successful Open Source Software community around
KMS technologies.
• Contributions to relevant open source software projects including GStreamer,
OpenWebRTC and Chrome.
• Publication of one journal paper and two conference papers, as shown in the
NUBOMEDIA Communication and Dissemination Results deliverable.
• Submission of two journal papers, as shown in the NUBOMEDIA
Communication and Dissemination Results deliverable.
• Contributions to relevant industrial conferences as shown in the NUBOMEDIA
Communication and Dissemination Results deliverable.
As part of the last year of the project, we plan to generate the following outcomes:
• Creation of a startup leveraging KMS for creating an innovative business model
for RTC in the cloud.
• Submission of, at least, one scientific publication to a top journal in the area of
multimedia tools and applications presenting the final KMS architecture and
benchmarks on its internal workings.
5.1.5 References
[AHMAD2005] Ahmad, Ishfaq, et al. "Video transcoding: an overview of various
techniques and research issues." Multimedia, IEEE Transactions on 7.5 (2005): 793804.
[AJANKI2011] Ajanki, Antti, et al. "An augmented reality interface to contextual
information." Virtual reality 15.2-3 (2011): 161-173
[AMIR1998] Amir, Elan, Steven McCanne, and Randy Katz. "An active service
framework and its application to real-time multimedia transcoding." ACM SIGCOMM
Computer Communication Review. Vol. 28. No. 4. ACM, 1998.
[AMIRANTE2013] Amirante, Alessandro, et al. "On the seamless interaction between
webRTC browsers and SIP-based conferencing systems." Communications Magazine,
IEEE 51.4 (2013): 42-47.
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[AMIRANTE2014] Amirante, A., et al. "Janus: a general purpose WebRTC gateway."
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5.2 Real-time Video Content Analysis on the cloud
Computer Vision (CV) is just one of the technological fields with higher growth and
with growing popularity nowadays. Wikipedia defines computer vision as:
“... a field that includes methods for acquiring, processing, analyzing, and understanding images and, in
general, high-dimensional data from the real world in order to produce numerical or symbolic
information, e.g., in the forms of decisions.
Therefore, we can say that its objective is to determine what is happening in front of a
camera and use that understanding to control a computer or system, or to provide people
with new images that are more informative or aesthetically pleasing than the original
camera images. Nowadays, Computer Vision applications can be used for a variety of
use cases across many industries. Some examples of usage of computer-vision
technology include video surveillance, biometrics, automotive, photography, movie
production, Web search, medicine, augmented reality gaming, new user interfaces, and
many more.
There are several examples which show the increasing popularity of this technology.
In 2014, Google launched the Project Tango Smartphone [TANGO] a project that uses
computer vision and other technologies to achieve the goal of giving mobile devices a
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human-scale understanding of space and motion. This technology tracks the motion of
the device in 3-D and at the same time creates a map of the environment. According to
the Project Tango website, the sensors in Project Tango devices allow the device to
make over a quarter million 3-D measurements every second, updating its position and
orientation in real time, combining that data into a single 3-D model of the space around
you. Google also acquired Jetpac, a startup that has created a mobile application that
uses computer vision to extract and analyze data from public Instagram photos. The
data generated from the photos is used to create city guides. The Jetpac City Guides app
is a visual guide for more than 6,000 cities worldwide.
But Google is not the only tech giant who is investing ion Computer Vision. Oculus
VR, the Facebook subsidiary responsible for the latest virtual-reality revolution, just
acquired the technology startup Surreal Vision [SURREAL]. Surreal Vision is focused
on real-time 3D scene reconstruction – generating an accurate representation of the real
world in the virtual world. Great scene reconstruction will enable a new level of
presence and telepresence, allowing you to move around the real world and interact
with real-world objects. Another example which shows the bet by computer vision has
been the acquisition by Facebook of Instagram. Microsoft is also investing efforts in
computer visions, as it is shown in one of their last research reports [MSIMGCLASS],
where they claim that one Microsoft’s team developed a system that can classify images
of the ImageNet database with an error rate lower than human’s.
But tech giants are not the only ones who are betting on this technology. It is
increasingly common to find SMEs and startups that make the computer vision one of
their core competences. Apart from those companies who have been already acquired
by large players, we can find others who still survive on their own. Some examples are
as follows
•
•
•
Tire check [TIRE] who tries to detect whether the wheels of the vehicles have
proper air pressure through a picture taken with your smartphone
Tyris software [TYRIS] who uses CV to extract depth measures, track objects
or identify people and implements some of these algorithms in the cloud.
Seene [SEENE] who has developed a portfolio of advanced computer vision
algorithms designed from the scratch for use on mobile devices in real-time
applications.
Not only the industrial sector is very interested in CV, but also it is a very active
research field. For example, if we search in Google Scholar the term “computer vision”,
we find 73,800 results only in 2015. To name a few:
•
•
•
Motion compensation based fast moving object detection in dynamic
background [ZHANG2015]
Practical matters in computer vision [JAIN2015]
FaceNet: A Unified Embedding for Face Recognition and Clustering
[SCHROFF2015]
However, Computer vision is computationally expensive. Computer vision or Video
content analysis algorithms (VCA) consume a prohibitive amount of resources in
particular memory and CPU. That is the reason why many problems in the field cannot
be solved properly. For example, complex algorithms require many functions operating
in parallel on a single image to extract all relevant content, and in many cases the users
require that the algorithm processes the images in less than 40 milliseconds.
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The emergence of media servers in a cloud environment in the last years is considered
to be one of the enabling technologies that allow some complex VCA algorithms to be
used in real applications. Running in the cloud VCA algorithms that can inter-operate
with each other and that are accessible through comprehensive yet simple APIs can
open new horizons to computer vision applications.
Some of the benefits that cloud computing technology offers to computer vision are:
• Computational power and storage.
• Allows for having VCA algorithms or services on demand.
• Publishing algorithms for massive use through simple APIs
Nevertheless, as we can see in the rest of this section, it does not appear to exist any
open source mature solution for running video content analysis (VCA) algorithms in
media servers on the Cloud.
5.2.1 Description of current SoTA
In this section, we focus on the current available commercial products, FOSS solutions
and scientific SoTA which run computer vision algorithms in the cloud. Prior to that,
we will describe the main libraries which can be used as base technology for these
cloud-based products or services. Although many companies or communities have
partially based their developments in their own technology, however in many cases
these solutions rely on a set of common libraries. The most popular libraries are as
follows.
•
•
•
OpenCV (Open Source Computer Vision) [OPENCV] is a library of
programming functions mainly aimed at real time computer vision. It is
probably the most famous and used library. It is cross-platform and can run over
different platforms such as Windows, Linux, android iOS and MacOS. The
library is written in C and C++. It also contains wrappers for other languages
such as Python and Java. OpenCV has a specific module which provides GPU
acceleration in order to run more accurate and sophisticated algorithms in realtime on higher resolution images. This library is free for use under the opensource BSD-license. It helps developers to build different applications such as
facial recognition, gesture recognition, segmentation, stereo vision, motion
tracking, object identification and motion robotics.
SimpleCV (Simple Computer Vision) [SIMPLECV] is an open source
framework for building computer vision applications. With it, you get access to
several high-powered computer vision libraries such as OpenCV – without
having to learn about bit depths, file formats, color spaces, buffer management
or matrix versus bitmap storage. This is “computer vision library made easy.”
SimpleCV is free to use, and it is open source. It is written in Python, and runs
on Mac, Windows, and Ubuntu Linux. It is licensed under the BSD license.
PointCloud Library (or PCL) ) [PCL] is a library for 2D/3D image and point
cloud processing. The PCL framework contains numerous state-of-the art
algorithms including filtering, feature estimation, surface reconstruction,
registration, model fitting and segmentation. These algorithms can be used, for
example, to filter outliers from noisy data, stitch 3D point clouds together,
segment relevant parts of a scene, extract key points and compute descriptors to
recognize objects in the world based on their geometric appearance, and create
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•
•
•
surfaces from point clouds and visualize them -- to name a few. PCL is released
under the terms of the 3-clause BSD license and is open source software. PCL
is cross-platform, (runs on Linux, MacOS, Windows, and Android/iOS).
VLFeat [VLFeat] is an open source library which implements popular computer
vision algorithms in image understanding, local features extraction and
matching. It is written in C for efficiency and compatibility, with interfaces in
MATLAB for ease of use, and detailed documentation throughout. It supports
Windows, Mac OS X, and Linux. Some of the algorithms included are: Fisher
Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative
information bottleneck, SLIC superpixels, quick shift superpixels, large scale
SVM training, and many others.
BoofCV is an open source Java library for real-time computer vision and
robotics applications. Written from scratch for ease of use and high
performance. Its functionality covers a wide range of subjects including,
optimized low-level image processing routines, camera calibration, feature
detection/tracking, structure-from-motion, and recognition. BoofCV has been
released under an Apache 2.0 license for both academic and commercial use.
CCV is another Computer Vision library which tries to be much easier to
deploy, the code is better organized with a bit more dependency hygiene. It now
runs on Mac OSX, Linux, FreeBSD, Windows, iPhone, iPad, Android,
Raspberry Pi. In fact, anything that has a decent C compiler probably can run
it. CCV includes different functionalities that will help the developer to build
applications requiring image classifiers, frontal face detectors, cars and
pedestrian detectors, text detectors, and object tracking. Its source code is
distributed under BSD 3-clause License.
Figure 33: The most popular Computer Vision libraries
Defining better the technology and interfaces of cloud media servers will be crucial to
see different options of computer vision libraries supporting cloud computing.
Commercial computer vision products and solutions in the cloud
First of all, it is important to highlight that every developer could create a media server
with computer vision functionalities on the cloud from the scratch. For that purpose,
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developers can use one of the libraries that we have just explained and deploy it in a
cloud provider such as Amazon web services, Microsoft azure or Google cloud.
Maybe, the most difficult part will be to send the video stream from the client side to
the cloud provider. For the video streaming you can use GStreamer which is a
multimedia framework written in C which can serve as a base to create many types of
multimedia applications such as streaming media broadcasters, media players and video
editors. Here [OPENCVAMAZON], you can find a post which explains how to run
OpenCV services using PHP on Amazon cloud.
As for the commercial solutions, the Microsoft Windows Azure may be one of the
most famous platforms which offer computer vision processing on the cloud. The
computer vision part is based on a Microsoft’s special project called Microsoft Project
Oxford [MSOXFORD] which offers a set of services for understanding data and adding
‘smart features’ to your application. They offer a number of APIs related to:
• Faces: Face APIs provide state-of-the-art algorithms to process face images, like
face detection with gender and age estimation, face recognition, face verification
through which they can match the face of a person in two different images, face
grouping through which they can classify a set of unknown faces based on
similarities. A famous example of an application built using this API is the web
site www.how-old.net which is a demo that detects faces and estimates the
person age.
• Image analysis: with this API, the user can extract low-level features such image
categories, dominant color and more from the input image’s visual content.
• Get Thumbnail: given an input image this API generates a high quality image
and stores an efficient thumbnail. This functionality uses smart cropping for
thumbnails that are different than the aspect ratio of your original image to
preserve the region of interest.
• OCR: Optical Character Recognition detects text in an image and extracts the
recognized characters into a machine-usable character stream.
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Figure 34: Microsoft Project Oxford
Visual Tools had the opportunity to create a simple application to test the use of this
APIs over Microsoft Windows Azure to find some relevant limitations. Specifically, we
have created an application to detect faces and the result is that it takes several seconds
to process a single image. Another important thing is that we have not found how to use
this API with a video stream.
The past December 2, 2015, Google launched a Computer Vision API [GVISIONAPI]
over its cloud, Google Cloud platform. The Google Cloud Vision API allows
developers to build powerful applications that can see, and more importantly understand
the content of images. All the functionalities offers to the users are exposed through an
easy-to-use REST API. So far the prices to use this API over the Google Cloud have not
been set, Google offers a limited preview, since it is an API that just came out. The API
offers the following features:
• Label/Entity Detection picks out the dominant entity (e.g., a car, a cat) within
an image, from a broad set of object categories. You can use the API to easily
build metadata on your image catalog, enabling new scenarios like image based
searches or recommendations.
• Optical character recognition, to retrieve text from an image. Cloud Vision
API provides automatic language identification, and supports a wide variety of
languages.
• Safe Search Detection, to detect inappropriate content within your image.
Powered by Google SafeSearch, the feature enables you to easily moderate
crowd-sourced content.
• Facial Detection can detect when a face appears in photos, along with
associated facial features such as eye, nose and mouth placement, and likelihood
of over 8 attributes like joy and sorrow. The API does not support facial
recognition and does not store facial detection information on any Google
server.
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•
•
Landmark Detection to identify popular natural and manmade structures, along
with the associated latitude and longitude of the landmark.
Logo Detection to identify product logos within an image. Cloud Vision API
returns the identified product brand logo.
Another example of commercial products in this field is provided by vision.ai
[VISIONAI]. Vision.ai is a company who builds computer vision products and services.
They offer a product call VMX vision server which provides object recognition,
detection and tracking. They give you the opportunity to make a local installation or use
a Docker container to deploy it on the cloud.
A good example that reinforces the theory that IT companies are investing heavily in
computer vision is IBM. IBM acquired the Alcheamy company [ALCHEAMY] last
March (2015). Among other things Alcheamy offers two different computer vision
products running over Bluemix, the IBM cloud platform. These products offer:
• Face Detection and recognition API, through this services when an image or
URL is provided, the system returns the estimated age, gender, and in the
case of the celebrities the identities of the people in the photo.
• Image Tagging, through this service when an image or URL is provided, the
system returns keywords summarizing scenes, objects and stylistic features.
The API is capable of identifying 3D objects such chars, dogs, building,
recognize scenes for example streets, stores, beaches, landscapes, mountains
and detecting people and faces. We have tested this functionality through the
demo of its web site, and we have to say that works pretty well.
However, these applications have also the same limitation already identified in other
solutions, i.e. they do not give support to video streams. In addition, it takes a little time
to process the images. In the same line as Alcheamy, we found Aylien [AYLIEN]
which also provides an API for tagging images.
Another solution which brings computer vision services to the cloud has been launched
by Meta Mind [METAMIND]. MetaMind was founded in December 2014 with the goal
of providing artificial intelligence-as-a-platform that were easily accessible and easy to
use. The company is focusing on the development of a new type of natural language
processing, image understanding and knowledge base analytics platform. MetaMind
utilizes groundbreaking technology called Recursive Deep Learning. The MetaMind
API provides image classification, but in the same way as the others examples we can
use the API only with single images.
Having reviewed commercial products and solutions we will review FOSS solutions
here after (free and open source software solutions).
FOSS (Free Open Source Software) solutions in the cloud
There are some existing initiatives providing computer vision in the cloud. The first one
which is worth to highlight is CloudCV [CLOUDCV] a large-scale distributed
Computer Vision as a cloud service. The CloudCV is an open source project coming out
of the Graphlab [GRAPHLAB] project in the summer 2012. The first version of
CloudCV was available in summer 2013. This first version only provided an image
stitching algorithm in order to combine multiple photographic images with overlapping
fields of view to produce a segmented panorama or high resolution image. After this
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first version the CloudCV has introduced new algorithms. Currently, the algorithms
which can be executed are:
•
•
•
•
•
Image stitching
Object detection
Object classification, through which different objects in the image can be
automatically identified.
Decaf: A deep convolutional activation feature for generic visual recognition.
Face detection and recognition. In fact, they have a demo capable of finding
important people on images.
The following figure depicts a basic scheme of CloudCV.
Figure 35: CloudCV Architecture
CloudCV provides “computer vision algorithms as a service” to researchers, students
and app developers through its Matlab, python and web APIs. GraphLab, OpenCV,
VLFeat and Caffe are the libraries and frameworks which provide a high level
programming interface, allowing a rapid deployment of distributed machine learning
algorithms. The deployment is done over different cloud platforms, Amazon web
services [AWS], Windows Azure [AZURE] and Virginia Tech clusters
[VIRGINIATECH].
In the following figure we can see an example of how this platform works. The webservers are responsible for listening to incoming job requests and sending real-time
updates to the user. A job scheduler takes these incoming jobs and distributes them
across a number of worker nodes. Here, it is important to highlight that if the job
scheduler detects a task requiring a lot of processing power, it will send it to a node with
graphics processing units (GPU) based on Nvidia hardware in order to improve the
performance of the task.
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Figure 36: CloudCV backend
After reviewing the CloudCV platform, we are going to present another platform which
tries to bring computer vision algorithms to the cloud. This platform is called Rubix.io
[2]. At the time of this writing Rubix.io is still in beta. This software can be used
through a Ruby API. A JavaScript API is under development. In this beta version, you
can use through its API the following algorithms:
•
•
•
•
•
Object Detection.
Text recognition (OCR).
An algorithm to detect Image similarities.
They are currently working on a face recognition algorithm.
Vibrand is a product for google glasses which allow you to collect images of
brand logos and products and provide relevant information about them.
However, at the time of this writing Rubix.io does not seem under active development,
e.g. more than a year has passed without any commit to its repository at Github.
Scientific publications
As we have explained on the previous section a lot of interesting research in computer
vision is underway. A search in Google Scholar provides quite some results. In contrast,
the number of scientific publications on computer vision AND cloud computing is
much less. The most relevant ones are described here after.
Applications or prototypes on face recognition or license plate recognition over cloud
are some of the research examples. In particular, one can find many examples of Face
Recognition in the net. For instance, in the paper “Face Recognition for Social Media
with Mobile Cloud Computing” [INDRAWAN2013] a cloud solution is proposed for
face recognition using mobile devices. In this solution the mobile devices are in charge
of detecting the face on the image. Once the face has been detected, the part of the
image where the face has been detected is sent to the cloud service to perform the face
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recognition. Another example of facial recognition in the cloud can be found in the
paper “Cloud-Vision: Real-Time Face Recognition Using a Mobile-Cloudlet-Cloud
Acceleration Architecture” [SOYOTA2013]. The challenge of this study lies with how
to perform task partitioning from mobile devices to cloud and distribute computing load
among cloud servers to minimize the response time considering communication
latencies and server computing powers. However, in this challenge the image
processing is carried out in the mobile devices or some internal servers which are found
in the previous step to send information to the cloud. In fact, with the aim to avoid
becoming very network-intensive, the system only sends metadata corresponding to the
HAAR features of the face. This metadata will be used to make the matching between
the info sent and the corresponding databases of faces. A further example that follows
the line of using facial recognition in the cloud is explained in the paper “Biometric
Authentication and Data Security in Cloud Computing” [MASALA2015]. This paper
presents a new Cloud platform designed to support basic web applications and
guaranteeing secure access. The platform is built using the OpenStack architecture,
while the user authentication is based on an original biometric such as face or
fingerprint recognitions. In the same way as the previous examples, the system does not
send any image to the cloud platform, but the mathematical model computed by the
biometric device.
Regarding License Plate Recognition, the paper “Cloud Based Anti Vehicle Theft by
Using Number Plate Recognition” [GAETHA2014] presents a specific system for
efficient automatic theft vehicle identification by using the vehicle number plate. The
proposed algorithm follows the typical phases in license plate recognition: Vehicle
identification, Extraction of number plate region, Recognition of plate characters and
OCR. In this system the only part which is executed on the cloud is the OCR. Another
example of a system based on the four steps on license plate recognition (LPR)
mentioned above is an LPR in the cloud developed Visual Tools. It consisted of a
vehicle identification system developed in the Itea project “Web Of Objects” [WOO].
The system was designed to operate without real-time requirements and only with
images and not video streams.
5.2.2 NUBOMEDIA approach beyond SotA
As we saw in the previous section we have seen that the computer vision is fast growing
technology having large companies, SMEs and researchers behind the scene. However,
we have realized that there is a considerable lack of information about computer
vision in the cloud. This may be due to Cloud computing being a relatively new
technology with high potential but it still needs to be further exploited in certain fields
like computer vision. In the examples we have seen related to specific solutions, some
applications process images outside of the cloud and then send the metadata or isolated
images at a specific time but not as a continuous stream. Other systems work over
images and not over a video stream and they do not support real-time requirements. As
for the platforms that try to bring cloud-computing algorithms to the cloud, Rubix.io
seems not to be very active. It does not provide many algorithms and does not specify if
the platform also works for a video stream. In the same way, they do not mention
anything about real-time results. Finally, CloudCV also provides too few computer
vision algorithms. However, it seems have some capabilities to process video streams
with real-time results. But even if there is somewhat more information on this platform,
it is still scarce and we have many issues to solve such as whether you can apply various
algorithms over the same video stream, whether they will develop more algorithms in
the future or what is the communication standards supported for real-time
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communication. Maybe, CloudCV can be a competitor for the NUBOMEDIA project.
Therefore, the consortium will be alert to follow the evolution of that platform.
As a result of the previous review, NUBOMEDIA emerges as a remarkable solution to
create a cloud platform specifically designed for real-time interactive multimedia
services supporting VCA and Augmented Reality. Apart from the benefits of cloud
technology on VCA algorithms, the main positive impacts that the NUBOMEDIA
project can have are:
• Providing a simple to use API encapsulating and abstracting the complexities of
VCA technologies. This API makes possible to create applications just by chaining
individual media functions known as “Media Elements.” The creation of such
chains with different VCA services is suitable for tackling complex problems. For
example, combining a motion detector media element and a face recognition media
element can be done with the media pipeline (chain of media elements) shown
below. In this way, every time the motion detector detects motion in an image the
face detector will try to detect faces on that particular image.
•
•
•
•
NUBOMEDIA enables many intelligent operations on video to be executed in realtime, an essential feature in many application areas such as video surveillance or
video games.
NUBOMEDIA is Free Open Source Software (FOSS). This guarantees that the
platform is open and can be openly accessed in order to create a community of
contributors. Therefore, the number of VCA services, elements or algorithms could
be widely increased generating a big library of computer vision functionalities.
Combining VCA with Augmented Reality will enrich the number of useful
applications to build up.
Ease of chain creation and composition which allow for fast development of
customizable applications
In addition, building VCA systems is beyond the reach of people without deep
understanding of image processing, modeling and expertise in computer programming.
These prerequisites limit the ability of creating VCA-based applications to a reduced
community of researchers and advanced programmers and serve as a high barrier of
entry for developers without skills in these areas.
5.2.3 NUBOMEDIA outcomes
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On this section we will describe the different outcomes of NUBOMEDIA related to
Computer Vision. At the time of writing this deliverable (end of December 2015), the
filters/algorithms supported by NUBOMEDIA is as follows.
• NuboFaceDetector
• NuboMouthDetector
• NuboNoseDetector
• NuboEyeDetector
• NuboEarDetector
• NuboTracker (Object Tracker)
• NuboVfence (perimeter intrusion detector)
• NuboMotion (Motion detector)
Apart from these filters, we expect to develop at least one filter more within the current
release (January 31st, 2016).
Based on these filters and the characteristics of the NUBOMEDIA platform and taking
into account the solutions and platforms described in this document, we can conclude:
•
•
•
•
NUBOMEDIA contains a higher number of algorithms, compared to open
source platforms and different business solutions studied on this deliverable.
NUBOMEDIA allows for great flexibility in creating applications, by providing
the ability to create different pipelines with the filters provided. This outcome is
particularly relevant, since we have not found any similar capability on other
open source platforms and applications studied.
NUBOMEDIA does not only provide Computer Vision over the cloud, it also
provides Augmented Reality and media capabilities enabling users to develop
more complete solutions.
Unlike other solutions analyzed above, NUBOMEDA gives support to video
streams.
5.2.4 References
[TANGO] https://www.google.com/atap/project-tango/
[SURREAL] http://surreal.vision/
[TIRE] http://tirecheckapp.com/
[TYRIS] http://tyris-software.com/
[SEENE] http://seene.co/
[OPENCV] www.opencv.org
[SIMPLECV] http://simplecv.org/
[PCL] http://pointclouds.org/
[VLFEAT] http://www.vlfeat.org/
[BOOFCV] http://boofcv.org/
[CCV] http://libccv.org/
[OPENCVAMAZON] https://abhishek376.wordpress.com/category/computer-vision/
[MSOXFORD] https://www.projectoxford.ai/
[GVISIONAPI] https://cloud.google.com/vision/
[VISIONAI] http://vision.ai
[ALCHEAMY] http://www.alchemyapi.com/
[AYLIEN] http://aylien.com/
[METAMIND] https://www.metamind.io/
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[AWS] https://aws.amazon.com
[AZURE] https://azure.microsoft.com/en-us/
[VIRGINIATECH] http://www.phys.vt.edu/facilities/clusters.shtml
[CLOUDCV] www.cloudcv.org
[RUBIX] www.rubix.io
[GRAPHLAB] http://graphlab.com/index.html
[WOO] http://www.web-of-objects.com
[ZHANG2015] Wei Zhang, et al. "Motion compensation based fast moving object
detection in dynamic background" Volume 547 of the series Communications in
Computer and Information Science pp 247-256.
[JAIN2015] Lakhmi C. Jain , et al. "Practical matters in computer vision" Volume 547
of the series Communications in Computer and Information Science pp 247-256.
Computer Vision in Control Systems-2 Volume 75 of the series Intelligent Systems
Reference Library pp 1-10
[SCHROFF2015] Florian Schroff, et al. " FaceNet: A Unified Embedding for Face
Recognition and Clustering" Google. Computer Vision and Patern Recognition
Conference 2015.
[INDRAWAN2013] Prasetyawidi Indrawan, et al. "Face recognition for social media
with mobile cloud computing." Journal on Cloud Computing: Services and Architecture
(IJCCSA),Vol.3, No.1, February 2013.
[SOYOTA2013] Prasetyawidi Indrawan, et al. "Cloud-Vision: Real-time Face
Recognition Using a Mobile-Cloudlet-Cloud Acceleration Architecture" Computers and
Communications (ISCC), 2013 IEEE Symposium.
[MASALA2015] G.L. Masala, et al. "Biometric Authentication and Data Security in
Cloud Computing" International conference on security and management (SAM’2015)
[GAETHA2014] Gaetha B.G , et al. "Cloud Based Anti Vehicle Theft by Using
Number Plate Recognition" International Journal of Engineering Research and General
Science Volume 2, Issue 2, Feb-Mar 2014 ISSN 2091-2730.
5.3 Augmented Reality capabilities on real-time media servers
5.3.1 Description of current SoTA
In augmented reality (AR) digital objects are overlaid to the real world. The extended
real world is viewed through a device having a display and camera. The rendered virtual
objects on top of the real world view can contain etc. 2d/3d images, video, text, sound.
The technology used in AR contains various enablers, but in extreme simplification of
the process, only two things are required: recognition of the point where the object is
rendered and the rendering of the object.
Starting at year 2000, the AR started to evolve strongly towards consumer applications,
such as digital extension into a printed advertisement. The history of AR applications is
more concentrated on mobile device applications for a personal user rather than a
technology for video communication. During the year 2015 there has been remarkable
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acquisitions of companies developing AR technology: Vuforia was bought by PTC (a
global provider of technology platforms and solutions that transform how companies
create, operate, and service the “things” in the Internet of Things, IoT) 2015, Metaio
was bought by Apple 2015 and 13th Lab bought by Facebook (Oculus Rift Division).
Also a company developing new era AR, Magic Leap, raised over 500 M USD for
developing their AR technology. This builds expectations for wider adoption of the AR
technologies. Same trend is supported from the vast development of new generation see
trough AR glasses, including Sony SmartEyeGlass, MS Hololens and Magic Leap.
Since AR is researched quite a long time there are quite a few solutions for AR libraries
that are either commercial or open source. Naturally the commercial libraries are often
more advanced compared to the opens source libraries and during the last few years the
gap between them has become larger. The commercial solutions for providing AR
technology can be found from Table 8 and open source solutions from Table 9.
In general, for creating AR experience applications, native software for each platform is
required. For this reason, all the libraries, especially the commercial ones, support many
platforms, from Windows and iOS to all possible mobile operating systems. There is a
slight difference is between commercial and open source versions that almost all the
commercial products support also upcoming AR glasses. There exist some extensions
of AR libraries that are trying to tackle the supporting of multiple platforms. Web based
approaches are done by Cordova having a plugin from Wikitude and ARToolkit’s
JavaScript version JSARToolkit. Total Immersion’s technology is used in In2AR that
supports Adobe AIR’s native application development for multiple platforms.
As earlier said, the simplest AR system would contain only tracking and recognition. To
obtain any reasonable user experience for AR application actually the tracking of the
position where the object will be rendered is required. The traditional approach for
detecting the position of augmented item is to use specific markers, but it can also be a
planar (any image). In mobile devices, this is supported often via multiple cues: gps,
gyroscopes, compass, and most advanced use camera based 3D tracking (Simultaneous
Localization And Mapping, SLAM). As the 3D range sensors will miniaturize they will
become common as well for augmented reality purposes. The support for recognition
and tracking of images, shapes, positions via different sensors and rendering
possibilities also vary with in the existing AR libraries.
Commercial solutions for AR
Total Immersion
http://www.t-immersion.com/
Platforms
Desktop,
iPhone, iPad and
Android Phones
and
Tablets).
Adobe Flash
Special feature
Marker
Less
tracking as well as
unmatched
Face
Tracking
capabilities. 2D and
3D content
D'Fusion is
the world's
most widelyused
commercial
Augmented
Reality
solution.
Alvar
Mobile SDK for Support for full 3D
http://virtual.vtt.fi/virtual/proj2 iOS, Android, tracking
/multimedia/alvar/
Symbian,
Maemo, Flash
and Silverlight
platforms,
Layar
iPhone,
2d/3d
animation
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www.layar.com
Aurasma
http://www.aurasma.com
BlipAR
Wikitude:
http:// www.wikitude.com
In2AR
android,,
BlackBerry
tools
animation,
video,
audio,
and
3D
content.
iOS, Android,
Uses
Windows
and
technology
wearables,
by Layar
iOS, Android, 3d with SLAM
Windows
and
wearables,
iOS , android,
AR by Flash and
AIR
SDK
and
content
creation
support to Unity 3d
Table 8. Commercial solutions for Augmented reality
It is worth of noting that within few years many commercial AR players have bought by
big companies, which means that expectation for the technology is high. Below are the
famous examples:
• Vuforia bought by PTC (a global provider of technology platforms and
solutions that transform how companies create, operate, and service the “things”
in the Internet of Things (IoT).) 2015
• Metaio bought by apple (2015)
• 13th Lab bought by Facebook (Oculus Rift Division)
Open-Source
AR Platforms
technology
Argon
Argon
is
http://argon.gatech.edu/
currently
designed for
the iOS (3.1);
an Android
version is in
progress
Alvar
http://virtual.vtt.fi/virtual/
proj2/multimedia/alvar/
Linux,
windows
ArToolkit
http://www.hitl.washingto
n.edu/artoolkit/
Windows,
Linux,
MacOs
also
Android,
Flash
or
Silverlight
Special
Features
javascript
framework
for
adding
augmented
reality
content
to
web
applications
Licence
Apache
Uses Vuforia
2.0 Open Tracking
Source
License.
GNU
Lesser
General
Public
License,
version
2.1
duallicense:
GPL,
commerc
ial
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First
opensource AR
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JSARtoolkit
https://github.com/kig/JS
ARToolKit
HTML5
webRTC
ArUco
http://www.uco.es/investi
ga/grupos/ava/node/26
Windows,
Linux
JavaCV
https://github.com/bytedec
o/javacv
ATOMICAuthoringTool
http://sourceforge.net/proj
ects/atomic-project/
Java
/
Android
interface
Microsoft
Windows,
Ubuntu and
Mac OS X.
Windows
XP, Vista, or
7
and
Windows
Phone 7.5
GoblinXNA
https://goblinxna.codeplex
.com/
+ JavaScript
port
of
FLARToolKi
t
GRATF
http://www.aforgenet.com
/projects/gratf/
Mixare
http://www.mixare.org/
Android/iPho
ne
PTAM, Parallel Tracking Linux,
and Mapping for Small Win32, OSX
AR Workspaces
https://github.com/Oxford
Marker based
GNU
General
Public
License
BSD
GPLv2
Inherits from
ARtoolkit
(FLARToolKi
t is the Flash
Actionscript
(v3) version of
ARToolKit)/
demo
with
webRTC
support exists
Based
on
OpenCV,
Trivial
integration
with OpenGL
and OGRE.
Based
on
ArToolkit,
Open CV
GNU
GPL
Goblin
XNA
License.r
tf
Microsof
t
Permissi
ve
License.r
tf
(Latest
release
27.6.201
2)
Marker
GNU
based, 2D/3D GPL v3
augmentation (Latest
release
06.03.20
12)
Marker
GPLv3
based, gps
(latest
2D
commit
augmentation August
2012)
SLAM
GPL
(latest
commit
5.11.201
NUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
Based
on
ALVAR
marker-based
camera
tracking
package,
InterSense
hybrid
trackers.
supports the
Vuzix iWear
VR920
Only for non
commercial
use
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-PTAM/PTAM-GPL
3)
DroidAR
Android
https://github.com/bitstars/
droidar
GeoAR
https://wiki.52north.org/bi
n/view/Projects/GeoAR
BeyondAR
http://beyondar.com/home
Location,
GPL
marker
based,
3d
objects
Android
Geospatial
Apache
data , 2d 2.0
License
support
(latest
version
March
2013)
Android and
Apache
google glass
2.0
License
Table 9. Opensource AR software
Augmented Reality technology is used in many ongoing research projects. The closest
to NUBOMEDIA approach are EU projects FIWARE and Compeit, where media server
technologies and AR are developed and utilized.
• FIWARE, where an open source platform for and supporting sustainable
ecosystem around it is developed. AR is part of the platform, Java Script
support is provided by JSAR open source library as well as VTT ALVAR
commercial library.
• Compeit, concentrates creating highly interactive, personalised, shared media
experiences
Other on going or recent projects example for AR are
• iAM project http://www.iam-project.eu/, heritage and tourist domain
• Satifactory, http://www.satisfactory-project.eu/, industry 4.0
• Lara http://lara-project.eu/index.php/project-overview, navigation and
position technology
• Target Training Augmented Reality Generalised Environment Toolkit,
http://cordis.europa.eu/project/rcn/194852_en.html, gaming
• Insiter http://www.insiter-project.eu/, construction, refurbishment and
maintenance of energy-efficient buildings
• Venturi https://venturi.fbk.eu/, pervasive AR, user centric design
5.3.2 NUBOMEDIA approach beyond SotA
Currently most of the AR applications and platforms offer their solutions so that the
processing of all required functions are performed locally. This due to the fact that the
one of the fundamental ideas in AR solutions has been consumer applications where the
user itself is looking the world via mobile device and gets new view augmented by
virtual elements. Transferring the augmented view for others has not been the main
emphasis in the field with the exception of adaptive advertising in sport broadcast
events. The first AR solutions were based on markers, but now more and more
technology is available for recognizing e.g. planars, buildings and objects from real 3D
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world via camera and other sensors. This requires much more computational power and
thanks to new era of cloud computing the trend is to split tasks on the client and server
side. For example Gammeter & al [2] published a system for mobile AR where object
recognition is performed on the server side and tracking on the client side. In planar
(image based) type city AR where recognizable object can be merely anything, this is
the only possible approach to search a match from millions of objects. The tracking is
implemented utilizing both image and sensor based methods. Distributed architectures
are also used by commercial solutions, e.g. Wikitude provides cloud based recognition
of images.
The consideration of using AR in videoconferencing or telepresence applications opens
totally different view on designing the architecture for such applications. In video
communication cases the video stream is processed by the media server and this allows
extending more complicated processing to be done at the server side, such as rendering
which currently almost always done at the client side. In [1] there is a proposed an
architecture for enhancing a video service. In this architecture the AR overlay is
performed on the server side and higher rate output of video as well as more accurate
positioning is expected as there is more computational power available.
Rendering at the server side is not common in AR applications. But for example
Playstation already has a cloud gaming service, where all the processing for interactive
virtual content is done at the cloud server. The game is then streamed as a video to the
viewing device. One benefit of that is to allow very wide selection of mobile devices
with incompatible properties, e.g. hardware for 3D acceleration to enable PS gaming.
In [1] they also made a comparison of device-side and server-side AR videophone,
table[3] below from [1].
We can also identify other benefits of the server-side AR
• AR content management eases out, as the required material for rendering does not
need to be locally stored, also no need to download all the content to be rendered
• Larger virtual models (e.g. 3d models) can be handled at the server side
• Content can be changed on the fly
• In advertisement type of scenario, the content management is at the server side and
easily controlled
• Video streams are always supported and incompatibles devices for rendering have
access to augmented video stream
• Customization of the rendered image -> in multipoint communication the rendered
content can vary between each participant
One of the well-known bottlenecks in AR has been introduction of the AR solutions for
the end user. Web-RTC protocol is changing the playground for AR and communication
systems by bringing standardized means of real time communication to the web
applications. For the user WebRTC supported service means, that there is no
(necessary) need for a special application download for the AR enabled service. Only
access to internet and browser is enough. AR enhanced videoconferencing system
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would need only opening a web page, not always downloading and installing specific
application for this, but only opening a link at the browser. This will tackle down one
obstacle for AR to become mainstream as supported device population is extremely
large. For the creators and developers this means web based development of
applications.
OoVoo (http://www.ooVoo.com) is a company providing WebRTC enabled
communication services and SDK for developing them. They already have elementary
video filters for enhancing the video communication with video effects or e.g. by adding
a 2d images over/top of the face based on face position. In their solution the rendering is
performed on the client side.
5.3.3 NUBOMEDIA outcomes
Augmented reality functionality in NUBOMEDIA is providing the media server AR
capabilities based on marker and planar recognition and tracking, as well as supporting
the rendering of 2D / 3D content. This will provide a unique platform for using
webRTC enabled implementation and supporting distributed AR application
architecture with server side rendering, detection and tracking.
In NUBOMEDIA the rendering will take place on the server side. This opens a potential
to new type of applications and business models. The rendered content can be very
complex as massive 3D scene descriptions are not transferred between the server and
client. On a distributed server architecture, load balancing allows different content
rendering for each participant in multipoint video communication. This for example
introduces a new way of advertising where the ads can be embedded in video stream
and personalized by each person’s browser history.
Innovation of AR in NUBOMEDIA is on the usage of the AR technology in new way in
multimedia communication, enabling easy to develop AR applications to the very large
audiences without the need of installing specific AR applications.
5.3.4 References
[1] Fukayama, A.; Takamiya, S.; Nakagawa, J.; Arakawa, N.; Kanamaru, N.; Uchida,
N., "Architecture and prototype of augmented Reality videophone service," in
Intelligence in Next Generation Networks (ICIN), 2011 15th International Conference
on
,
vol.,
no.,
pp.80-85,
4-7
Oct.
2011
doi:
10.1109/ICIN.2011.6081108
[2] Gammeter, S.; Gassmann, A.; Bossard, L.; Quack, T.; Van Gool, L., "Server-side
object recognition and client-side object tracking for mobile augmented reality," in
Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer
Society Conference on , vol., no., pp.1-8, 13-18 June 2010
5.4 Interoperability on real-time media infrastructures servers
Interoperability has always been the corner-stone of the Telecom networking ideas,
where each carrier or service provider complies with well-structured agreed standards.
The web world achieves compatibility by de-facto prevailing mechanisms that can
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morph and evolve much faster, but particular services are dominated by few large
players, who require all users to join, with no inter-service interworking.
Over the last decade over the top services (OTT), such as instant messaging (IM) and
Voice over IP (VoIP), have revolutionized the well-established services and business
models of real-time media providers. However, applied communication standards and
protocols are mainly used to create closed ecosystems, resulting in a highly fragmented
market of mostly isolated communication platforms that restrict free, open and
interoperable communication flows. Currently, telecommunication architectures are
based either on the Telco federation or on a global “Private Walled garden” Market
models:
● Telco Federated distribution model: universal interoperability and a highly
regulated market constrain service delivery agility to geographically limited
markets. However, this model ensures consumers well defined expectations in terms
of reachability among users (independently of its service provider domain), trust in
service providers and service quality.
● “Private Walled Garden” distribution model: used by popular players like
Google, Skype and WhatsApp (aka Over The Top - OTT) that have much more
agility to deliver cost-effective (mostly free) innovative services to borderless
markets. However, OTT players are creating silos of users where only intra-domain
interoperability is ensured (for example, a WhatsApp user can only send a message
to another WhatsApp user, a Skype user can only call to another Skype user or use
Skype’s breakout service to the PSTN). In this model, service developers and thirdparty service providers are forced to use proprietary APIs and incur difficulties if
they attempt to sell and distribute services to different domains and are limited to
the user base of the service provider. In general, services are developed within a
specific service domain, constraining service reachability between different service
domains. Trust in service providers is more and more questionable and becomes a
concern; the service delivery is unregulated and has to rely on best-effort Internet
connectivity in all cases
5.4.1.1 Description of current SoTA
5.4.1.2 IMS and VoLTE
The IP Multimedia Subsystem (IMS) is currently the state of the art in terms of
standardized Service Architecture for Telecommunication Services. It is an evolution of
the Intelligent Network (IN), but based on Internet Protocol (IP) and Information
Technology (IT) principles. IMS has been specified by 3GPP in Rel. 5 onwards, as
summarized in TS23.228 [3GPP] as the core packet network session control. It supports
a layer of communication services that replaces the TDM based IN.
The goal for IMS was to provide Telecom operators a secure, trustworthy session
control environment that operators and 3rd party service providers can plug their
services into, e.g., Joyn/RCS [GSMA]. However, due to IMS complexity and the
traditional restrictive architecture, service development is slow and cumbersome. At the
same time, the technological and business-related innovations in the Internet space have
generated a wealth of attractive services, with greater agility and low cost base. These
services run over the enhanced 4G infrastructure that is now providing high bandwidth
(LTE), and this is exploited by OTT (Over The Top) players, locking out the carriers.
Telecommunication service providers are struggling to match the agility and innovation
of web based services, even when they replace conventional Telecommunication
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services, while the changes in communication habits and user behaviour, e.g. texting
and social networking, encourage the shift of users towards web based facilities.
The introduction of VoLTE (IMS over LTE) requires a complete core network
replacement based on the 3GPP Evolved Packet Core (EPC) for end-to-end QoS
provisioning in LTE networks, with IMS as the session control and service environment
for Voice/Video conversational services. This is perceived as a huge effort (manpower,
project management, CAPEX, OPEX), while the prospects of new revenues in the
increasingly competitive environment of real-time communication services are
diminishing.
Figure 37 shows the IMS layered architecture [40], where the layers of session control
and service logic have been separated, unlike previous architectures of circuit-based
switches and even softswitches. This enables the calling applications to operate
independently, while activating the IMS core for session control. Therefore, the current
rolling out of VoLTE (IMS over LTE) provides an excellent opportunity for web
applications to ‘grab’ the front-end, while still perform like carriers’ IMS systems.
As shown, the media plane is also an independent layer, but it is subject to the session
control that determines the media parameters (QoS, priority, security). The media runs
directly between the communicating points, while session control signalling traverses
the network to the core servers.
Also shown is the range of management functions. Besides CRM, Billing and
Provisioning as well as network management, it shows that new facilities for hosting
network services are now enabled, due to the modularity of IMS. The implementation of
the IMS platform for a large service provider benefits from the ideas of SOA, as
described in [COPELAND_SOA_2009]. The SOA principles can help to provide
efficient distributed IMS core, built of modular components that can be scaled
independently from each other. It follows that what makes IMS so complex is also the
reason for its flexibility and openness.
Figure 37 IMS Layered Architecture
The monolithic approach to communication, where devices, access, backhaul, core and
applications were all under a single communication service provider is no longer valid.
With the advent of LTE and the EPC (Evolving Packet Core), the access network has
become an independent layer, hence the IMS core can serve multiple types of access
networks. The application layer is also independent, but is still very constraint by the
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IMS interfaces. As we are now facing new definition of communications within the
Internet space using web technologies, it is important to inherit the achievements of
IMS while attempting to surpass it in other respects.
In Figure 38 and Figure 39, [WEBRTCHACKS] attempts to integrate the webRTC
client with the P-CSCF, the IMS user agent function, and converge data from the
webRTC portal with its authentication system with HSS user data repository.
Figure 38 WebRTC integration with IMS user agent and data repository
This approach allows IMS functions to be used in session control for webRTC sessions.
While this may not be entirely desirable for those who seek new ways of controlling
sessions, the requirement to interconnect with IMS is unavoidable, given the growing
numbers of IMS systems being installed.
In Figure 39, the NNI (Network to Network Interface) connects elements of the
webRTC architecture to IMS gateways. Note that the IBCF (Interconnection Border
Control Function) converts webRTC signalling on the fly, just as it does for H.248 to
SIP, and that the Translation Gateway (TrGW) converts media codecs on the fly, for
webRTC as well as IMS media.
Figure 39 webRTC linking to IMS via NNI
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While the IMS core can continue to provide QoS-managed service delivery even when
the front-end application is web based, carriers who implement IMS are still constraint
by territorial licensing. In addition, the Telecom Industry has failed to provide attractive
communication services that match the agility and innovation of the web players. The
IMS support of applications is not easy for web developers who find the service
interaction via SIP too difficult. SIP, which has been born as a simple way of invoking
Voice over Internet, has been extended greatly, and is now perceived as difficult to
work with.
The service environment for IMS that integrates HTTP based services, as specified by
OMA (Open Mobile Architecture), is given in Figure 40 [COPELAND2009]. It shows
the main interface to services – the ISC (IMS Service Control) which is SIP based,
connecting to service enablers that deal with compatibility and interworking, before
linking to application. Although this architecture is comprehensive, it is clear that it
does not encourage rapid service development.
Application
Application
Application
Server
I0
AS
I0
ESI-A
Enabler Server I/f (AS)
Enabler Server I/f (AS)
I0/I2
I2 type of interfaces
I0/I2
Ut
ESI-B
Server Enablers
Dh
Sh
ISC
SLF
HTTP
Proxy
S-CSCF
P-CSCF
HSS
Rf
Ut
Ro
OCS
HTTP
Proxy
CCF
IMS Core
Gm
UE
UE
Gm
ETI-2
ETI-1
Enabler
Terminal I/f
Terminal Enablers
Mb
Bearer
Enabler
Terminal I/f
Mb
Figure 40 OMA based IMS service architecture
5.4.1.3 WebRTC Media
Taking a web-centric service perspective on IMS, the complete standard and
environment may be considered somehow anachronistic. The value of IMS lies clearly
on trust, security, accountability and the integration of managed networks but does not
address requirements from an open service environment, such as programmability,
extensibility and ease-of-use.
With HTML5, and WebRTC features, the Communication Industry have an opportunity
to remodel the full Core Communication Services Infrastructure, unifying
communication Web and IoT towards a more agile, simple and open environment.
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Applying RESTful APIs and light policy driven SOA Governance principles, would
lower the barrier, facilitate cloud deployment, and of course the composition with Web
applications.
However, WebRTC does not specify how communications are established (e.g. from
signalling perspectives). Different initiatives behind the IETF have been adopted to
standardize some signalling protocols for WebRTC, but there is no agreement across
vendors. SIP over WebSocket, JSON, proprietary APIs and SDKs, standard APIs are
options already available by different vendors.
WebRTC services will also require:
•
Opening network functionalities to developers and users. This will be done
by standard-based API and SDKs that will help to avoid vendor lock-in and
bring a huge quantity of web developers to the world of real time
communications. This way, easy-to-use APIs will be available soon.
•
Managing quality of service and efficiency with measurement tools. There
will be valuable tools to define, implement and test, and some cost-efficient
mechanisms to improve the quality of communications.
•
Building services that deal with all the security concerns. Different services
offered as OTT have suffered security threats and attacks, due to the
immaturity of their business model, company background, and issues related
to lack of ownership to the layers below applications (multi-layer policy
servers or firewalls cannot be used here).
•
WebRTC does not mandate the context in which real time communications
are used.
5.4.1.4 Signalling protocols and Gateways
Interoperability of WebRTC communications and SIP based systems is an inevitable
requirement, given the roll out of VoLTE/IMS in both wireline and wireless networks.
In [SINGH2013], a method of ‘SIP in JavaScript’ is proposed, with two options:
•
In an Endpoint Browser approach, the SIP stack runs in JavaScript in
browser, using a SIP proxy server to support Web Socket;
•
In a network gateway at web server, it is proposed to have a special gateway
to enable interworking. This gateway can be hosted by web provider, VOIP
provider or an independent third party.
[SINGH2013] argues that signalling over Web Sockets (SIP in JavaScript) and media
over WebRTC allows keeping tools separate from the applications, which enhances
scalability and flexibility. As the last resort, a network gateway capable of translation is
used for transcoding signalling and media.
Figure 41 shows the proposed SIP-webRTC gateway, where highlighted areas indicate
the differences.
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Figure 41 Comparing SIP proxy with SIP-webRTC Gateway
Note, that while SIP is a proxy, the proposed SIP-WebRTC gateway is a B2BUA (Back
to Back User Agent), which implies special resident intelligence and decision making,
rather than merely represent the user actions to the network.
5.4.2 NUBOMEDIA approach beyond SotA
To fully achieve interoperability on real-time media infrastructures is a big challenge
because, these infrastructure apply communication standards and protocols, the effect
being creation of mainly closed ecosystems, resulting in a highly fragmented market of
mostly isolated communication platforms that restrict free, open and interoperable
communication flows.
5.4.2.1 Interoperability Requirements
An alternative method that improves on the integration of web communication
applications is required. The NUBOMEDIA project must surpass the IMS alternative in
several ways:
•
Front-end web communications apps must have easy access to session
control, without the constraints of the IMS service architecture interfaces
(ISC etc.).
•
Web technologies and protocols are assumed to be used by web developers,
for example – using REST and resource based architecture instead of
extended Diameter for access to service data.
•
Different web communication application may be used by the parties in a
single session, each supporting different sets of session features (Caller ID,
Do-not-Disturb, no-return-calls, bar last caller).
•
New applications and modified applications must be able to address users’
devices without having to undergo end-to-end testing, i.e. new features may
not work in some cases, but will not disrupt the whole network, while a safe
mechanism is provided for updating apps clients.
•
APIs to network facilities, such as QoS policy, Charging/billing and
enhanced authentication, must be standardized, so that web apps do not need
to change their service logic to link to different CSPs.
•
A network gateway capable of translation is required for transcoding
signalling and media
To fulfill all the above requirements within the NUBOMEDIA project is impossible
because it requires standard support of creation and integration of gateways binding
both the telco and web domains which is out of scope of the project.
Nevertheless, a proof-of-concept implementation demonstrating the interoperability on
the signalling plane of a WebRTC based infrastructure and an IMS infrastructure. This
approach allows IMS functions to be used in session control for WebRTC sessions. The
media path integration on the other hand is still not fully interoperable.
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5.4.3 NUBOMEDIA outcomes
Fraunhofer FOKUS has implemented an IMS Connector which provides the possibility
to be used as an application server on the IMS network infrastructure or an IMS proxy
masking a WebRTC client as a User Agent on the IMS network. The IMS Connector
itself provides a grad variation in operation and can operate in both modes at the same
time. The software artifact are found on the public fhg-fokus-nubomedia github
repository [FOKUS_GITHUB].
5.4.4 References
Referencing websites
[3GPP]
3GPP TS 23.228, IP Multimedia Subsystem (IMS); Stage 2: [Online].
Available: http://www.3gpp.org/DynaReport/23228.htm
[GSMA]
GSMA Rich Communications Suite, Specs & Product Docs, [Online].
http://www.gsma.com/network2020/rcs/specs-and-product-docs/
[WEBRTCHACKS] V. Pascual in https://webrtchacks.com/a-hitchhikers-guide-towebrtc-standardization/
Referencing papers or books
[COPELAND_SOA_2009] R. Copeland. SOA Case Study – the BT IMS OSIP.
BT Technology Journal 2009
[COPELAND2009] R. Copeland. Converging NGN Wireline and Mobile 3G
Networks with IMS. ISBN 978-0-8493-9250-4, Taylor & Francis 2009
[SINGH2013] Singh, K.; Krishnaswamy, V., "A case for SIP in Javascript,"
Communications Magazine, IEEE , vol.51, no.4, pp.28,33, April 2013
5.5 Cloud APIs for accessing Media Servers
Analysts such as Marc Andreessen claim that “software is eating the world” stressing
the importance of software-centered models into the economy and the transition of
traditional business to software-based organizations [ANDREESSEN2011]. This trend
is permeating into all areas of IT (Information Technologies) including also multimedia
industries. In the last few years, we have witnessed how multimedia technologies have
been evolving toward software-centered paradigms embracing cloud concepts through
different types of XaaS (Everything as a Service) models [CATHERINE2013].
More recently, another turn of the screw is taking place thanks to the emergence and
popularization of APIs (Application Programming Interfaces). This is perfectly
summarized by Steven Willmott with his claim “software is eating the world and APIs
are eating software” [WILLMOTT2013]. Software developers worldwide are getting
used to create their applications as a composition of capabilities exposed through
different APIs. These APIs are typically accessible through SDKs (Software
Development Kits) and expose in an abstract way all kind of capabilities including
device hardware, owned resources and remote third party infrastructures. This model,
applied to cloud concepts, is quite convenient for individual developers and small
companies, which have now the opportunity of competing with large market
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stakeholders without requiring huge effort investments and without needing to acquire
hardware infrastructure or software licenses. Thanks to this, in the last few years, we are
experiencing an explosion of innovation with thousands of new applications and
services both for WWW and smartphone platforms that are being catalyzed by the rich
and wide ecosystems of APIs made available to developers.
This trend towards the “APIfication” is also invading the multimedia arena and, very
particularly, the RTC (Real-Time multimedia Communications) area. Initiatives such as
WebRTC [JOHNSTON2012] are bringing audiovisual RTC in a standard and universal
way to WWW users. The main difference between WebRTC and other popular videoconferencing applications is that WebRTC is not a service, but a set of APIs enabling
WWW developers to create their customized applications using standard WWW
development techniques.
WebRTC belongs to the HTML5 ecosystem and has awakened significant interest
among the most important Internet and telecommunication companies. As opposed to
other previous proprietary WWW multimedia technologies, it has been conceived to be
open in a broad sense, both by being based on open standards and by providing open
source software implementations. Currently, a huge standardization effort on WebRTC
protocols is taking place at different IETF working groups (WGs), being the RTCWeb
WG the most remarkable one [RTCWEB2015]. In turn, WebRTC APIs are being
defined and consolidated at the W3C WebRTC WG [WEBRTC2015]. WebRTC
standards are still under maturation stage and they might take some time to consolidate.
In spite of this, most of the major browsers in the market already support WebRTC and
it is currently available in billions of devices providing interoperable multimedia
communications.
Hence, WebRTC is an opportunity for the creation of a next generation of disruptive
and innovative multimedia services catalyzed worldwide through those emerging APIs.
However, to reach this goal, the WebRTC ecosystem needs to evolve further. Basing on
WebRTC browser capabilities, services can only provide peer-to-peer communications,
which restrict use-cases to simple person-to-person calls involving few users. In order to
enhance this model, server side infrastructures need to be involved. This is not new: as
it is well known, the traditional WWW architecture is based on a three tier model
[FRATERNALI1999] involving an application server layer and a service layer, this
latter typically reserved to databases. In the same way, rich media applications also base
on an equivalent three tier model where the service layer provides advanced media
capabilities. The media component in charge of providing such capabilities is typically
called media server in the jargon.
Media servers are a critical ingredient for transforming WebRTC into the next wave of
multimedia communications and the availability of mature solutions exposing simple to
use yet powerful APIs is a necessary requirement in that area. However, most
standardization and implementation efforts are still concentrated at the client side and
server side technologies are still quite fragmented. Although there are a relevant number
of WebRTC media servers available they do not provide coherent APIs compatible with
WWW development models and developing with them typically requires expertise with
low level protocols such as SIP [ROSENBERG2002], XMPP [SAINTANDRE2011] or
MGCP [ANDREASEN2003], on which average WWW developers do not have any
experience. In addition to this, most state-of-the-art media WebRTC media servers just
provide the three basic capabilities specified above and are extremely hard to extend
with further features. However, nowadays, many RTC services involve person-toNUBOMEDIA: an elastic PaaS cloud for interactive social multimedia
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machine and machine-to-machine communication models and require richer multimedia
processing capabilities such as computer vision, augmented reality, speech analysis and
synthesis, etc.
5.5.1 Related work
The commoditization of RTC media server technologies brought increasing interest on
more flexible mechanisms for media control. Several IETF WG emerged with the
objective of democratizing them among common developers. As a result, protocols such
as MSCML [VANDYKE2006], MSML [SALEEM2010] emerged providing the ability
of controlling media server resources through technologies understandable and familiar
to average developers such as XML and [BRAY1998].
Although these protocols are simpler to understand and integrate, developing
application on top of them is still a cumbersome, complex and error prone process. Due
to this, many stakeholders noticed that the natural tools used by developers are not
protocols but APIs and SDKs. Hence, a number of initiatives emerged trying to
transform the protocol-based development methodology into an API-based development
experience providing seamless media server control through interfaces adapted to
programming languages specificities and not to infrastructure characteristics. In
particular, the Java platform was one of the first on integrating this philosophy by trying
to reproduce the WWW development experience and methodology for the creation of
RTC media enabled applications. A relevant activity in this area is JAIN (Java API for
Integrated Networks), which issued several APIs for the signaling, control and
orchestration of media capabilities. These include the JAIN SIP API
[ODOHERTY2003] the JAIN SLEE [FERRY2004] API and the JAIN MEGACO API
[BAJAJ2004]; this later being specifically devoted to control media servers through the
H.248 protocol. JAIN APIs did not permeated much out of operators, but their ideas
inspired more popular developments such as the SIP Servlet APIs
[KRISTENSEN2003], for the signaling plane, and the Media Server Control API (aka
JSR 309) [ERICSON2009] for the media plane, which have been more widely used for
the development of RTC solutions for voice and video.
Among all these APIs, this document is especially interested in the JSR 309. JSR 309
concepts were quite revolutionary at the moment because the API tried to fully abstract
the low level media server control protocols and media format details. The objective
was to enable developers to concentrate on application logic. For it, JSR 309 defined
both a programming model and an object model for media server control through a
northbound interface, but independent of media server control protocols and hence,
without requiring any specific southbound protocol driver. JSR 309 does not make any
kind of assumption in relation to the signaling protocol or to the call flow, which are left
to the application logic.
From a developer’s perspective, probably the most innovative concept of JSR 309 was
the introduction of a mechanism for defining the media processing logic in terms of a
topology. This mechanism is based on an interface called Joinable. In JSR 309, all
objects having the ability to manipulate media (e.g. send, receive, process, archive, etc.)
implement such interface, which has a join method enabling interconnecting such
objects following arbitrary dynamic topologies. Hence, a specific media processing
logic can be implemented by developers just joining the appropriate objects. As an
example, if you want to create an application mixing two RTP (Real-time Transport
Protocol) streams and recording the resulting composite into a file, you just need to join
the appropriate objects with the appropriate topology. Taking into consideration that in
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JSR 309 the NetworkConnection is the class of objects capable of receiving RTP
streams, that MediaMixer is the class of objects with mixing capability and that
MediaGroup is the class with be ability of recording; the above mentioned media
topology can be achieved just by joining two NetworkConnection instances to a
MediaMixer instance, which in turn, is joined with a recording MediaGroup. This
approach makes possible for developers to conceive their media processing logic as
graphs of “black-box” joinables, which is a quite modular and intuitive mechanism for
working in abstract terms with the complex concepts involved in RTC multimedia
applications.
Another relevant innovation of JSR 309 is the introduction of media events. Thanks to
this mechanism, the media processing logic held by a media server can fire events to
applications through a publish/subscribe mechanism. This is very convenient for
enabling applications to become media-aware meaning that complex processing
algorithms at the media server can provide asynchronous information dealing with
things happening inside the media, for instance DTMF (Dual-Tone Multi-Frequency)
tones being detected, voice activity being present, and so on.
JSR 309 permeated into mainstream developer audiences as a suitable API for media
server control following the typical three tier model. However, in the last few years, the
emergence of novel technologies and computation paradigms have made JSR 309 to
show relevant limitations. For example, nowadays group videoconferencing services are
evolving from Media Mixing models, which require relevant media processing, towards
SFU (Selective Forwarding Unit) models, which are based on media routing
[WESTERLUND2016]. JSR 309 is heavily adapted to Media Mixing and, due to this,
most of its APIs assume that participants send/receive only one media stream to/from
the media server. As a consequence, SFU models do not fit nicely into JSR 309 APIs.
This is particularly a problem when all the streams of a group videoconference are
multiplexed into a single RTP session, as happens typically on modern WebRTC SFU
media servers supporting bundle RTP [JENNINGS2015] because JSR 309 APIs do not
provide any kind of mechanism for demultiplexing streams from a
NetworkConnection. Moreover, in JSR 309 the API specification explicitly forbids
several input NetworkConnections to be joined to a single output
NetworkConnection, as an SFU router would require. Instead, they need to be
joined first to a MediaMixer, which, in turn, can be joined to the output
NetworkConnection.
When looking to other modern RTC technologies, we notice again that the JSR 309
design has limitations. For example, if we consider WebRTC W3C APIs, we may
understand that they split endpoint capabilities into different functional blocks each of
which is exposed through an abstract interface (e.g. RtpSender, RtpReceiver,
PeerConnection, etc.) However, if we want to expose WebRTC media server
capabilities through JSR 309 we need to accept that endpoints can only be represented
through the NetworkConnection interface, which is extremely limited to support
rich WebRTC capabilities such as DataChannels [BECKE2013], Trickle ICE
[IVOV2014], simulcast [WESTERLUND2015], etc.
JSR 309 shows also drawbacks in relation to its extensibility. In JSR 309 it is possible
to support new media object types using MediaGroups, however, configuration of this
new types have to be done with Media Server specific descriptions as strings, which
cannot be validated by compiler. It is important to note that these new media object
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types cannot be NetworkConnection, only MediaGroups. This is a hard
limitation because no other network protocol different than RTP (negotiated through
SDP) can be incorporated. The ideal would be to allow supporting the creation of new
object types in a similar way than core types, with factory methods in MediaSession
(e.g. createNetworkConnection, createMediaGroup, etc.), but this is not
possible as MediaSession is an interface defined in JSR 309 API and hence it
cannot be modified by the API user.
Further limitations about JSR 309 are the following:
- A counter-intuitive asynchronous development model basing on an obscure
joinInitiate primitive, which is incompatible with modern Java mechanism
for managing asynchrony such as futures, continuations or lambdas. This lack of
clean asynchronous programming model makes JSR 309 difficult to adapt to
reactive programming frameworks and languages that are very demanded by
developers today such as Node.js or Scala.
- A complete lack of mechanism for monitoring and gathering quality stats on media
sessions. This is an essential ingredient for production systems.
- JSR 309 it is designed specifically for the Java language. It would be desirable a
portable API that can be used in as more languages as possible.
- This API is specially designed to control phone Media Servers because it expose
concepts like Dialogs (Prompt and record, DTMF, VoiceXML dialog, etc.). For
example, it is mandatory for an implementation to provide a player with the
capability to detect audio signals in DTMF, but this kind of functionality is not very
useful in web applications.
5.5.2 NUBOMEDIA approach beyond SotA
NUBOMEDIA approach for progressing beyond SotA is to create a novel API
complying with a number of requirements that, as described above, are not available in
current solutions. These progresses can be summarized as follows:
Seamless API extensibility through custom modules
We want developers to be able to plug additional capabilities to the API (e.g. processing
algorithms, protocols, etc.) and to enable their consumption as if they were native API
capabilities (i.e. without requiring different syntax or language constructs.) The
mechanism we require for this is based on modules in the sense that every extension
takes the form of a module artifact (e.g. a .jar file in the Java language, a .js file in
JavaScript language, etc.) and that developers may plug the modules they wish at
development time without requiring any further modification or configuration. Remark
that, for the reasons specified in sections above JSR 309 does not comply with this
requirement.
Adaptation to WWW technologies and methodologies.
This requirement has two aspects. The first, and most important, is the need of our API
to be adapted to novel RTC WWW technologies and very particularly to WebRTC. The
WebRTC architecture, based on heavy use of RTP bundle and RTCP multiplexing
mechanisms and requiring complex ICE management techniques such as Trickle ICE
makes complex to comply with this requirement. Also as specified in sections above,
JSR 309 is not compatible with this as the NetworkConnection is based on plain
RTP. The second, is the need of the API to adapt to the typical WWW three tier
development model. This means that the NUBOMEDIA Media API should be usable
for WWW developers with their common development, deployment and debugging
techniques and tools. To some extent, this means that the NUBOMEDIA Media API
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should be perceived by WWW developers as any other of the APIs consumed in the
application logic, such as database APIs or ESB (Enterprise Service Bus) APIs.
Full abstraction of media details (i.e. codecs and protocols)
Media representation and transport technologies are complex and require specialized
knowledge that is not typically available for common developers. For maximizing
productivity and minimizing development and debugging complexity the
NUBOMEDIA Media API should hide all the low level details of such technologies
through the appropriate abstractions. In doing so, these abstractions must maintain the
appropriate expressiveness enabling the API semantics to provide to developers the
ability of performing the required operations onto protocols and formats including
payloading, depayloading, decoding, encoding, re-scaling, etc.
Programing language agnostic
In today’s Internet, developers use a multiplicity of programming languages for creating
their applications. In fact, the majority of applications are called “polyglot” because use
different languages. The specific choice depends on factors such as the previous
experience, the personal preferences, the tasks to be accomplished, the target platform
or the required scalability. In this context, tying developers to a specific programming
language may be perceived as inflexible and unfriendly. For this reason, the
NUBOMEDIA Media API needs to be language agnostic and adapt to the most
common programing languages used nowadays. Of course, the specific syntax of the
API calls may differ depending on language specificities. However, this requirement
indicates that, somehow, the constructs, basic mechanisms and programming experience
needs to be the same across different languages. This means, for example, that a
developer having the appropriate expertise for creating applications with the Java
NUBOMEDIA Media API implementation should be able of doing so with a JavaScript
implementation as long as the subtleties of the two languages are known.
RTC media topology agnostic
One of the main objectives of RTC Media Servers is to provide group communication
capabilities to applications. Due to this, any useful NUBOMEDIA media API must
consider this as a central aspect of its design by exposing the appropriate constructs for
group communications. When looking to how RTC group communications are technical
implemented, we can notice that they are based on a set of well-known RTP
interconnecting topologies among which the most common ones are Media Mixing
Mixers (MMM), Media Switching Mixers (MSM) and Selective Forwarding Units
(SFU). In short, MMMs are based on the principle of composing a single output media
stream out of N input media streams, so that the final composite stream represents the
addition of the N input streams. MMMs require decoding of the N input streams, the
generation of the composite (e.g. linear adding in audio or matrix layout for video) and
encoding to generate the output stream. Due to the performance cost of these operations
MMM do not scale nicely. On the other hand, MSMs and SFUs do not perform any
heavyweight processing and they just forward and route N incoming streams to M
outgoing streams, reason why they have better scalability properties. Their only
difference is that MSMs enable the N to M mapping to change dynamically while on
SFUs it is static and the only possible operation is switching on/off forwarding on any
of the output M streams.
Understanding the differences and appropriate usage scenarios of these topologies is
complex and a source of extra complexity for application developers. Due to this, we
include a requirement for our NUBOMEDIA Media API to transparently manage all the
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subtleties of this problem so that the most appropriate solution is provided transparently
by the API. Remark that JSR 309 also tried to comply with this requirement through the
“Joinable” mechanism making possible for developers to establish topologies just by
joining sources with sinks. However, as explained above, both JSR 309, and
equivalently JSR 79, are only compatible with MMM topologies and cannot manage
the, by the way most popular, MSM or SFU models.
Advanced media QoS information gathering
QoS is critical in multimedia services. Some milliseconds of latency or jitter can be the
difference between a successful and an unsuccessful application. For this reason, RTC
media developers need to have the appropriate instrumentation mechanisms enabling
seamless debugging, monitoring and optimization of applications. This requirements
guarantees that our NUBOMEDIA Media API developers are able to access advanced
QoS metrics of the streams including relevant information such as packet loss,
bandwidth, latency or jitter. Remark that none of the above mentioned RTC media
server APIs, including the JSR 309, provide this kind of capability.
Compatibility with advanced media processing capabilities
So far, most RTC media technologies and APIs have been concentrated on the problem
of transport (i.e. taking media information on one place and moving it to other places.)
This happened because the most prevalent use case for RTC is person-to-person
communications, where end-users expect from technology to eliminate distance barriers
(i.e. to maintain a conversation as if it were face-to-face.) However, during the last
decade novel use cases involving person-to-machine and machine-to-machine
communications are gaining popularity in different verticals such as video surveillance,
smart cities, smart environments, etc. In all these verticals, going beyond plain transport
is a relevant requirement. As an example, the number of low latency RTC video
applications being used in security scenarios is skyrocketing. In all these applications
the ability to integrate Video Content Analysis (VCA) capabilities through different
types of computer vision algorithms is an unavoidable requirement. In addition, modern
media applications in areas such as gaming or entertainment complement VCA with
another trending technology: Augmented Reality (AR), which is also having high
demand from users. As a result, we include our NUBOMEDIA Media API to provide
full compatibility with these advanced processing techniques enabling their seamless
integration and use.
Context awareness
In RTC media services, as in other types of services, context is becoming a relevant
ingredient for providing added value to applications. Context is somehow an ambiguous
concept for which there is not yet a formal definition. However, most authors accept
context as any kind of information that can be used for characterizing the situation of an
entity. The OMA (Open Mobile Alliance) has generated a formal definition of context
through the NGSI standard [BAUER2010] as a set of attributes that can be associated to
an entity. When working with RTC media, the entity is most typically a RTC media
session (e.g. a media call).
Considering this context definition, this requirement means that our NUBOMEDIA
media API needs to be capable of consuming context for customizing and adapting enduser experience but, most important, need to be capable of extracting context attributes
from the media communication itself. In other words, the part of the context dealing
with the media itself (i.e. what the media content is and what it represents at any time)
needs to be manageable by the proposed API.
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Adapted to multisensory multimedia
Traditionally RTC media has referred to simple audiovisual streams comprising
typically one video track and one or two (i.e. stereo) audio tracks. However, moderns
trends and technologies extend this to a new multisensory notion [PARK2009], where
multisensory streams may comprise several audio and video tracks (e.g. Multi-view and
3D video) but may also enable the integration of additional sensor information beyond
cameras and microphones (e.g. thermometers, accelerometers, etc.) Hence, we establish
a requirement for our NUBOMEDIA Media API to be capable of managing such
multisensory multimedia in as seamless and natural way.
Adaptation to cloud environments
Cloud computing is permeating in all IT domains, including multimedia, as the de-facto
standard for system deployment and management. This trend is also permeating into
the RTC media server arena, reason why we need to consider it in the definition of our
API. Adapting the NUBOMEDIA Media API to cloud environments basically means to
make it compatible with how a PaaS (Platform as a Service) media server works
[VAQUERO2008] In other words, our API needs to be compatible with a new notion of
distributed media server, which in opposition with traditional monolithic media servers,
is distributed through a cloud environment and can elastically scale to adapt to endusers generated load.
5.5.3 NUBOMEDIA outcomes
The main NUBOMEDIA outcome in this project is the NUBOMEDIA Media API,
which is described in NUBOMEDIA Project Deliverable D5.2. This API complies with
the above mentioned SotA evolutions due to the following:
Seamless API extensibility through custom modules
The NUBOMEDIA Media API can be extended in a seamless way by using the RTC
Media Module mechanism, which provides full flexibility and no restrictions other than
extending from the base API classes.
Adaptation to WWW technologies and methodologies
The NUBOMEDIA Media API implementations fully comply with the traditional
WWW three tiered development model and enable developers to create applications
leveraging novel WWW RTC media technologies such as WebRTC in a seamless and
direct way.
Full abstraction of media details (i.e. codecs and protocols)
The NUBOMEDIA Media API makes possible to perform transparent transcoding
without requiring to worry about media internal details. This is due to the fact the
semantics of the connect primitive mandates the underlying media server capabilities to
perform all the appropriate adaptations in a fully transparent way.
Programming language agnostic
In our API, the only requirement for supporting a given programming language is to
specify how the API IDL is transformed into it and to implement the appropriate
compiler following that specification. In Deliverable 5.2 we provide such specifications
and describe their implementations in Java and JavaScript in the context of the Kurento
open source software project.
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RTC media topology agnostic
The NUBOMEDIA Media API makes possible to interconnect media elements
following arbitrary and dynamic topologies thanks to the connect primitive. This
means that developers do not need to be aware of the low level details of MMM, MSM
or SFU technologies: they just need to interconnect their endpoints, filters and hubs
accordingly to their needs. The API semantics shall translate these interconnections into
the appropriate low level mechanisms using MMMs, MSMs or SFUs in a fully
transparent way.
Advanced media QoS information gathering
The API exposes primitives fully compliant with the standard WebRTC “inboutrtp” and
“outboundrtp” stats.
Compatibility with advanced media processing capabilities
The concept of NUBOMEDIA API Filter provides this feature in a fully modular way
advanced media processing.
Context awareness
The notion of context emerges in quite a seamless through the NUBOMEDIA Media
API event mechanism, which makes it possible for media capabilities to publish events
to applications. These events may contain semantic information about the media content
itself. Hence, creating multimedia context-aware applications is straightforward: the
application logic just needs to subscribe to the relevant events and publish them into a
context database basing on NGSI or any other equivalent standard.
Adapted to multisensory multimedia
The NUBOMEDIA Media API can manage seamlessly arbitrary sensor data beyond
audio and video. This can be achieved through the combination of two features. The
first is the support for DataChannels that. The second is the fact that all streams
exchanges among MediaElements may have a DATA track. In particular, any
information received using DataChannels into a WebRtcEndpoint is published to the
rest of the pipeline through the endpoint’s source DATA track. In the same way, any
information received through the DATA track at a WebRtcEndpoint’s sink is send to
the network using DataChannels. As the MediaElement interface enables all the
information received through the DATA to be use by the element internal logic, this
mechanism makes possible, for example, to create Augmented Reality filters that
leverage sensor information for customizing the augmentation logic.
Adaptation to cloud media servers
The NUBOMEDIA Media API does not specify how media pipelines are placed into
media server instances. The API implementer has full freedom for selecting how newly
created media pipelines are scheduled. This flexibility can be leveraged by API
implementers to adapt to all kinds of cloud architectures. This has enabled specific
extensions that adapt transparently (i.e. without requiring modifying a single line of
code) applications created for Kurento Media Server to work into the NUBOMEDIA
PaaS.
These results have been useful for generating the following outcomes:
• Submission of two journal papers, one of them to the most important journal in
multimedia tools and applications.
• Creation and consolidation of an open source software community around
Kurento: a project belonging to the NUBOMEDIA ecosystem.
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During the last year of the project, we plan to submit one additional scientific
publication related with the NUBOMEDIA media API stack.
5.5.4 References
[ANDREESSEN2011] Andreessen M (2011) Why software is eating the world. Wall
Street Journal 20
[ANDREASEN2003] Andreasen F, Arango M, Huitema C, Kumar R, Pickett S, Elliott
I, Foster B, Dugan A (2003) Media gateway control protocol (MGCP) version 1.0.
Tech. rep., Internet Engineering Task Force, Request For Comments (RFC) 3435
[BAJAJ2004] Bajaj V (2004) JAIN MEGACO API Specification. Tech. rep., Java
Community Process, Java Specification Request (JSR) 79
[BAUER2010] Bauer M, Kovacs E, Schülke A, Ito N, Criminisi C, Goix LW, Valla M
(2010) The context API in the oma next generation service interface. In: Intelligence in
Next Generation Networks (ICIN), 2010 14th International Conference on, IEEE, pp 15
[BECKE2013] Becke M, Rathgeb EP, Werner S, Rungeler I, Tuxen M, Stewart R
(2013) Data channel considerations for rtcweb. Communications Magazine, IEEE
51(4):34-41
[BLACKWELL2000] Blackwell AF, Green TR (2000) A cognitive dimensions
questionnaire optimised for users. In: Proceedings of the Twelfth Annual Meeting of the
Psychology of Programming Interest Group, pp 137-152
[BRAY1998] Bray T, Paoli J, Sperberg-McQueen CM, Maler E, Yergeau F (1998)
Extensible markup language (XML). World Wide Web Consortium Recommendation
REC-xml-19980210 http://www w3 org/TR/1998/REC-xml-19980210 16
[CATHERINE2013] Catherine MR, Edwin EB (2013) A survey on recent trends in
cloud computing and its application for multimedia. International Journal of Advanced
Research in Computer Engineering & Technology (IJARCET) 2(1):304-309
[ERICSON2009] Ericson T, Brandt M (2009) Media Server Control API. Tech. rep.,
Java Community Process, Java Specification Request (JSR) 309
[FERRY2004] Ferry D, Lim S (2004) JAIN SLEE API Specification. Tech. rep., Java
Community Process, Java Specification Request (JSR) 22
[FRATERNALI1999] Fraternali P (1999) Tools and approaches for developing dataintensive web applications: a survey. ACM Computing Surveys (CSUR) 31(3):227-263
[IVOV2014] Ivov E, Marocco E, Holmberg C (2014) A Session Initiation Protocol
(SIP)
usage
for
Trickle
ICE
draft-ietf-mmusic-trickle-ice-sip-03.
https://tools.ietf.org/html/draft-ietf-mmusictrickle- ice-sip-03, accessed 12 December
2015
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[JOHNSTON2012] Johnston AB, Burnett DC (2012) WebRTC: APIs and RTCWEB
protocols of the HTML5 real-time web. Digital Codex LLC
[JENNINGS2015] Jennings C, Holmberg C, Alvestrand HT (2015) Negotiating media
multiplexing using the session description protocol (SDP) draft-ietf-mmusic-sdpbundle-negotiation23.
https://tools.ietf.org/html/draft-ietf-mmusic-sdp-bundlenegotiation-23, accessed 12 December 2015
[KRISTENSEN2003] Kristensen A (2003) SIP Servlet API. Tech. rep., Java
Community Process, Java Specification Request (JSR) 116
[ODOHERTY2003] O'Doherty P, Ranganathan M (2003) JAIN SIP API Specification.
Tech. rep., Java Community Process, Java Specification Request (JSR) 32
[PARK2009] Park S, Park NS, Kim JT, Paik EH (2009) Provision of the expressive
multisensory adaptation platform for heterogeneous multimedia devices in the
ubiquitous home. Consumer Electronics, IEEE Transactions on 55(1):126-131
[ROSENBERG2002] Rosenberg J, Schulzrinne H, Camarillo G, Johnston A, Peterson J,
Sparks R, Handley M, Schooler E (2002) SIP: session initiation protocol. Tech. rep.,
Internet Engineering Task Force, Request For Comments (RFC) 3261
[RTCWEB2015] Internet Engineering Task Force (2015) Real-time communication in
web-browsers (rtcweb). https://datatracker.ietf.org/wg/rtcweb, accessed 9 December
2015
[SAINTANDRE2011] Saint-Andre P (2011) Extensible messaging and presence
protocol (XMPP): Core. Tech. rep., Internet Engineering Task Force, Request For
Comments (RFC) 6120
[SALEEM2010] Saleem A, Xin Y, Sharratt G (2010) Media server markup language
(MSML). Tech. rep., Internet Engineering Task Force, Request For Comments (RFC)
5707.
[VAQUERO2008] Vaquero LM, Rodero-Merino L, Caceres J, Lindner M (2008) A
break in the clouds: towards a cloud definition. ACM SIGCOMM Computer
Communication Review 39(1):50-55
[VANDYKE2006] J. Van Dyke et al. (2006) Media Server Control Markup Language
and Protocol. Internet Engineering Task Force, Request For Comments (RFC) 4722.
[WEBRTC2015] World Wide Web Consortium (2011) Web real-time communications
working group. http://www.w3.org/2011/04/webrtc/, accessed 11 December 2015
[WESTERLUND2015] Westerlund M, Burman B, Nandakumar S (2015) Using
Simulcast
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draft-westerlund-avtcore-rtp-simulcast-04.
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December 2015
[WESTERLUND2016] Westerlund M, Wenger S (2016) RTP internet draft topologies
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[WILLMOTT2013] Willmott S, Balas G (2013) Winning in the API Economy.
Available online: http://www.3scale.net/wp-content/uploads/2013/10/Winning-in-theAPI-EconomyeBook-3scale.pdf
5.6 Real-time media APIs in smartphone platforms
5.6.1
Description of current SoTA
One of the objectives of the NUBOMEDIA project is to create smartphone APIs to
support developers for easily create mobile applications using NUBOMEDIA
capabilities and platform. The support is created for android and iOS mobile platforms
and thus the SoTA concentrates on the current implementation possibilities on these
platforms.
The area of real-time media APIs for smartphones is heavily populated and research on
this field has been widely active for years. For smart phones there is a large number of
commercial APIs adopting various technologies and protocols. Some examples of
commercial solutions that are available for multiple platforms are introduced below :
• Skype Developer Platform for Web [Skype] - Skype web SDK – is a set of
JavaScript components and HTML controls enabling developers to build
solutions which integrate a variety of real-time collaboration models. The
features include presence, chat, audio and video and can be used on various
browsing platforms and device endpoints. In the current version presence and
chat services are provided using REST-based web services while the support for
audio/video and application sharing is supported via downloadable plugin
available only for Windows 7 and 8 PCs and Macs. This meaning that the
services available for Android devices are limited to real-time presence and chat
data. The aim in the future is to provide support for standards such as SIP, SDP,
WebRTC as well as IP and PSTN voice.
• Google Hangouts API [Hangouts API] provides the programming interface to
Hangouts video calls. The API is implemented as a JavaScript interface that
enables listing of Hangout participants, sharing data between instances of the
app, controlling the user interface as well as the microphone, camera and
speaker settings.
• OpenTok [OpenTok] is an embedded communications platform supporting
WebRTC. The real-time communication features include video, audio,
messaging and screen sharing. OpenTok provides solutions for mobile devices
supporting iOS and Android SDKs.
• SightCall [SightCall]offers real-time video APIs and mobile SDKs for iOS and
Android platforms enabling embedding of video calls and video conferencing to
applications. The mobile libraries interact with the target device through three
low-level APIs: User Interface API to integrate in the host API and to provide
real-time communication, Audio/Video API to access and manage hardware
resources and Hardware events API to manage and monitor hardware and
device-specific events. The low-level APIs are designed to ensure that the
mobile SDKs remain device-agnostic and are easily adaptable. The SightCall
platform supports WebRTC and has integrated versions for Salesforce and
Zimbra.
• Plivo[Plivo] provides mobile SDKs for iOS and Android to integrate a voice call
capability to the mobile apps. The call feature is supported by Plivo data centres
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•
distributed globally. The webRTC SDK can be used to integrate voice
communication to browser based services.
The Apizee ApiRTC[Apizee]is a cloud platform offering live chat, voice and
video features to be integrated on websites and mobile applications. The
ApiRTC library is implemented to be utilized using HTML and JavaScript. The
platform implementation is based on WebRTC technology.
Similarly to commercial solutions there is a large number of FOSS real-time media
API’s as well. There are numerous communication libraries:
• Gstreamer [Gstreamer] is an open source multimedia framework for constructing
graphs of media-handling components. It supports applications from simple
audio/video streaming to complex audio mixing and video editing. Gstreamer is
released under the LGPL and works on all major operating systems such as
Linux, Android, Windows, Max OS X, iOS, as well as most BSDs, commercial
Unixes, Solaris, and Symbian. It has been ported to a wide range of operating
systems, processors and compilers. It runs on various hardware architectures
including x86, ARM, MIPS, SPARC and PowerPC.
• Apache Cordova [Cordova]is an open-source mobile development framework.
Apache Cordova enables using of standard web technologies such as HTML5,
CSS3, and JavaScript for cross-platform development, avoiding each mobile
platforms' native development language. Applications execute within wrappers
targeted to each platform, and rely on standards-compliant API bindings to
access each device's sensors, data, and network status.
• Mediastreamer2[Mediastreamer2] is a lightweight streaming engine specialized
for audio/video communication application. The open source library is
managing multimedia streams including audio/video capture, encoding and
decoding, and rendering. Liblinphone [Liblinphone] integrates the
Mediastreamer2 video call features into a single API.
In this context, NUBOMEDIA concentrates specially into WebRTC technologies. There
are existing open source possibilities to utilised WebRTC on mobile platforms where
most famous are Google’s WebRTC [WebRTC] and Ericsson’s openwebRTC
[openwebRTC] projects that provide the communication stack for android and iOS as
native code.
The support of WebRTC is something that is managed at a browser level. In iOS
platforms, mobile web browser Safari does not seem to fully support WebRTC, that
leaded to a number of initiatives aiming at solving someway this problem.
A first possible solution which is used to develop WebRTC application is the usage of a
plugin for Cordova (previously known as PhoneGap). The idea of Cordova is to let
developers build mobile applications using HTML, CSS, and JavaScript. At a very
basic level is like building a website, using a simple browser (known as WebView) as
application to renders files. However, WebView is limited and it often cannot give
developers full access to native hardware like camera, accelerometer, and other native
capabilities. Cordova’s strength is its ability to let developers leverage native hardware
of mobile devices through Cordova Plugins.
Cordova Plugins usually consist of a JavaScript file and all the necessary native code
files for each mobile platform ( Objective C files for iOS, Java for Android, C# for
Windows Phone, etc.) Methods defined in the JavaScript file call the methods defined in
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the native code, thus giving developers to access native functionality of mobile devices
after installing the Cordova Plugin and adding the provided JavaScript file. The plugin,
called OpenTok follows this paradigm: the native code creates a video view on top of
the Cordova WebView using native OpenTok SDKs, display the camera’s video stream
in the view, and stream the video to TokBox’s servers.
The Cordova plugin approach has been followed also by eface2face solutions, aiming at
offering a comprehensive tool for video-conferencing and electronic document signing.
Part of the code is released on github where a google RTCApp adapted to Cordova iOS
with HTML5 can be found together with a Cordova Plugin mapping the W3C
Javascript APIs on the eface2face solution.
A slightly different approach instead is the one followed by OpenWebRTC initiative,
aiming at offering “a mobile-first WebRTC client framework building native apps”. The
Solution, which is free and open source, has been driven by Ericsson Research it is
based on GStreamer multimedia framework and it is offered as Cross-platform, crossbrowser and fully supporting H264 and VP8 video Codecs.
The ambition of OpenWebRTC is to follow the WebRTC standard closely as it
continues to evolve. The bulk of the API layer is therefore implemented in JavaScript,
making it super fast to modify and extend with new functionality (one such candidate is
ORTC).
The initial version of the OpenWebRTC implementation was developed internally at
Ericsson Research over the last few years. OpenWebRTC and Bowser were released
publicly as free and Open Source in October of 2014. The well known Bowser App,
initially released in 2012 both for Android and iOS and the removed from stores till its
later release in October 2014 is a mobile App built on top of OpenWebRtc. Bowser uses
WebRTC in simple self-view and peer-2-peer audio/video chat and it is free on the app
store. Beside this, Bowser offers the possibility to manipulate Videos solving some UI
problems typical of the mobile side.
Other not-fully standard solutions are available such as PerchRTC which is open source
as well but it does not fully comply to the standard.
Specifically on Android platforms, it is possible to utilise a native WebView or third
party Crosswalk functionality for creating native applications using web/Javascript
development tools, including browser technology support, such as WebRTC. WebView
[WebView] is based on Chromium open source project and is a native component in
modern Android devices. Crosswalk [Crosswalk] is a HTML application runtime
platform to build android and Cordova apps with. It is focusing on mobile performance
and efficient use of device API’s.
These frameworks have originated from Android development. Currently the WebView
is integrated as a separated app to android operating system version 5.0 (lollipop) on,
but the support for WebRTC is only at Android 6.0 (Marshmallow). The great benefit
of using these is to be able to create fast prototypes for multiple environments. But on
the other hand the performance will never be same that in native applications. Also as
the apps are built on top of the framework utilising browser technologies it will have
some restrictions and usability might not reach the same levels as with native apps
trough out all the platforms.
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In the scientific community there is a large number released papers on the topics of
mobile multimedia communication, including communication protocols, audio/video
compression, networking technologies, distributed computing and multimedia service
development. The scientific forums related to mobile multimedia communications
include for example conferences such as ACM Multimedia 36 , IEEE International
Conference on Multimedia and Expo (ICME) 37 , The International Conference on
Mobile and Ubiquitous Multimedia (MUM) 38, EAI International Conference on Mobile
Multimedia Communications 39 and International Conference on Advances in Mobile
Computing and Multimedia (MoMM) 40.
In some recent publications authors have proposed extension for using WebRTC to
distribute all kinds of sensor data from mobile terminals. In [Azevedo2015] the authors
propose an extension to the WebRTC APIs to enable general access to sensors. The
approach is based on extending the functionality of JavaScript MediaStream API to
allow web applications to access sensor devices both in-device and off-device
configured into the browser. WebRTC is leveraged to enable the peer-to-peer
transmission of sensor data. Another approach to access the native layer of a
smartphone device is presented in [Puder2014]. Instead of defining an API, the services
on the smartphone interact with the HTML5 application via a specific protocol that
allows more flexible solutions, which can be implemented as a separate service running
on the smartphone or packaged with the app itself. The authors have implementations
for both Android and Windows Phone.
5.6.2 NUBOMEDIA approach beyond SotA
As it was shown in the previous paragraph, a lot of solutions are available, trying to
solve specific problems with different level of compliancy to the standards.
NUBOMEDIA aims at offering a comprehensive solution also on the client side,
including and extending all the functionalities offered by other solutions. An SDK, plus
related documentation and a sample “how-to-use” application is provided, able to
manage the WebRTC Stack and the connection to Kurento media server.
By enabling the communication with Kurento API and indirectly with Kurento Media
server, all the NUBOMEDIA functionalities can be consumed on the iOS client, and
this is indeed one of the main steps beyond the state of the art. As Some initiatives are
already providing software for setting up WebRTC communication between different
peers, the NUBOMEDIA access API for Room and Tree configurations are a something
that is peculiar of this initiative.
Even Bowser which seems to be the most complete solution offers just a simple selfview and a peer to peer audio/video chat, while NUBOMEDIA can take on mobile
different and more complex communication topologies. Offering a Native SDK for iOS
will overtake some of the limitations that quasi-Web-based solutions such as Cordoba
suffer in terms of full access to device functionalities. The Link to NUBOMEDIA will
also include in the stack all server-side functionalities which can be triggered Via API
by the client.
36
http://www.acmmm.org/2016/
http://www.icme2016.org/
38
http://www.mum-conf.org/2015/
39
http://www.mobimedia.org/2016/show/home
40
http://www.iiwas.org/conferences/momm2015/home
37
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The idea standing behind the release of a Native X-Code SDK offering primitives to
control connection procedures is also to give the possibility to any interested developer
to build his/her own app by using the code provided. Moreover, the connection to a
continuous integration system will ensure that any contribution won’t introduce
unwanted issues.
Another peculiar aspect of NUBOMEDIA for the smartphone platforms, which may be
very important for Telcos is the possibility to use potentially a custom signaling layer,
meaning that it is quite easy to Switch to SIP signaling protocol, allowing to extend the
set of functionalities available and to touch functionalities closer to the Telephony
world.
One to one
Topology
Room
Topology
Tree
Topology
iOS Native
Code SDK
Android
Native Code
SDK
Fully Open
Source
Media
Pipeline
Access
Bowser
✓
TokBox
✓
Talky
✓
Eface2face
✓
Nubomedia
✓
✘
✓
✓
✓
✓
✘
✘
✘
✘
✓
✓
✓Cordova
✓Cordova
✓
✓Cordova
✓Cordova
✓
✓
Opentok
✘
✓(otalk)
✓
✓
✘
✘
✓
✓
✓
✘
✘
✓
The table above summarizes the comparison among different solutions and shows the
main advantages of having an SDK connected to NUBOMEDIA solution with respect
to the state of the art.
The transversal advantage of NUBOMEDIA solution is the fact that the client side is
enabled to the media pipeline, being connected to the delivered PaaS Solution.
Moreover most of the Mobile SDK make use of Cordova Plugin which is a framework
for developing js/html-like application on the mobile side, helping code portability on
one side, but having some limitations on the other side.
Finally, we should remind that NUBOMEDIA mobile SDK will be tested over the
overall PaaS solution while developing demonstrator, establishing a kind of “software
package” tested and ready to be used.
5.6.3 NUBOMEDIA outcomes
NUBOMEDIA will provide native mobile SDK’s for iOS and Android.
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The first version of the iOS and Android SDK is already provided, as well as the first
version of the iOS and Android room and tree API’s and they will be maintained and
evolved till the end of the project also by means of the integration of a Continuous
Integration system.
5.6.4 References
[Apizee] https://apirtc.com/
[Azevedo2015] João Azevedo, Ricardo Lopes Pereira, and Paulo Chainho. 2015. An
API proposal for integrating sensor data into web apps and WebRTC. In Proceedings of
the 1st Workshop on All-Web Real-Time Systems (AWeS '15). ACM, New York, NY,
USA, , Article 8 , 5 pages.[Cordova] https://cordova.apache.org/
[Crosswalk] https://crosswalk-project.org/
[Gstreamer] http://gstreamer.freedesktop.org/
[Hangout API] https://developers.google.com/+/hangouts/api/
[Liblinphone] http://www.linphone.org/technical-corner/liblinphone/overview
[Mediastreamer2] http://www.linphone.org/technical-corner/mediastreamer2/overview
[OpenTok] https://tokbox.com/
[OpenwebRTC] http://www.openwebrtc.org/
[Plivo] https://www.plivo.com/
[Puder2014] Arno Puder, Nikolai Tillmann, and Michał Moskal. 2014. Exposing native
device APIs to web apps. In Proceedings of the 1st International Conference on Mobile
Software Engineering and Systems (MOBILESoft 2014). ACM, New York, NY, USA,
18-26.
[SightCall] http://www.sightcall.com/
[Skype]
https://msdn.microsoft.com/enus/library/dn962133(v=office.16).aspx[webRTC] http://webrtc.org/
[WebView] http://developer.android.com/reference/android/webkit/WebView.html
5.7 Cloud Videoconferencing APIs
Videoconferencing (VCF) is gaining relevance and its adoption in different
environments is increasing due to the economic and social advantages it provides. The
adoption of usable APIs (Application Programming Interfaces) -typically accessible
through SDKs (Software Development Kits)- is a trend increasingly demanded by
developers. The case of VCF is not an exception, and thus nowadays it is emerging
more and more initiatives aimed to provide VCFs APIs.
This section provides a brief state-of-the-art review on VCF APIs. Then, the main
advantages of the NUBOMEDIA approach for this domain are discussed.
5.7.1 Description of current SotA
The contribution of this section is two-folded. First, it reviews several scientific
contributions related to VCF APIs. Second, it presents a summary of some companies
and solutions that are investing effort in creating tools and solutions in this area.
We find an interesting scientific reference on VCF APIs on the paper by Rodriguez et
al. [RODRIGUEZ2009]. In this piece of research the authors propose a REST API for
integrating collaborative Videoconferencing as a Service (VaaS). This approach enables
the transformation of a standard client-server service into a Cloud Computing service.
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The approach presented in this work has been named “Nuve”. It is based on Adobe
Flex/Flash technology on the client side and Java on the server. The media server used
in Nuve is Red5, which allows for voice/video communications to be established among
the clients. The scalability is the main drawback of this work. It presents a prototype of
Nuve providing videoconference rooms where up only to six participants communicate
with each other via audio and video. This figure is quite small, and so Nuve cannot be
taken into consideration for a production-ready environment.
On the other hand, Gasparini et al. provides an insight to current trends on VCF,
classifying the existing approaches available in the literature into two types
[GASPARINI2013]:
1. Hardware-based (HW) VCF (also called dedicated systems) comprises VCF
solutions that require specific hardware-other than standard PCs, Macs and
smartphones.
2. Software-based (SW) VCF comprises VCF software tools that can be installed
and run on standard PCs, Macs and smartphones hardware.
Regarding SW VCFs, this work analyses the following tools:
• Skype [SKYPE] is a freemium VCF tool, owned by Microsoft.
• WebEx [WEBEX] is a freemium VCF tool for group sessions, owned by Cisco.
• Hangouts [HANGOUTS] is a free tool by Google, which can be used to
videoconference, to organize group sessions and to broadcast live events.
• Vidyo [VIDYO] and ViTAM [VITAM] require a server infrastructure to be
deployed and a client part that can be run on the web, iOS and Android.
• OpenMeetings [OPENMEETINGS] is an open-source project of the Apache,
which provides VCF and other tools for holding and managing
• OpenTok [OPENTOK] consists on server-side and client-side libraries for the
web, iOS and Android that need to be deployed or invoked to create customized
workflows.
• VSee [VSEE] consists of software-based VCF without the need for a server
deployment. Security is based on an end-to-end key negotiation and encryption.
This survey analyzes whether or not these tools meets some requirements, namely:
• Recording: some applications need to collect information or evidence.
• Deployment: need for a custom server deployment.
• Privacy and security: user information is subject to the compliance of several
national or regional data protection laws.
• Mobility: VCF needs to be available not only in standard PC and Mac devices
but also on mobile devices such as smartphones and tablets running Android,
iOS, BlackBerry or other operating systems.
• Interfaces: when VCF needs to be used by wide groups of population with
different technical backgrounds, interfaces need to be minimalist to increase
usability as much as possible.
• Workflow: sequence of actions that need to be carried out before a VCF session
can be established and during its progress until it is finished. This can be
classified in the following groups:
o Call: contacts lists are used to see whether users are online or offline. For
online users, a call request can be sent, which can be accepted or rejected
by the receiving endpoint.
o Group session: a group of users has a joint meeting in which all
participants can interact with the rest.
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•
o Broadcast: relies on the real-time transmission of a live event to the
whole world. Users can freely join and watch the event while interacting
with other users.
o Consultation with waiting room: replicates the typical rendez-vous
operation in real life, where patients with an appointment go to the health
premises and wait in a waiting room for the corresponding professional.
o Call center: through VCF, is prescreened by an intermediary prior to
being redirected to the most suitable interlocutor.
o Video surveillance for impaired users: VCF is used for remotely
monitoring users with mobility problems or other impairments.
Integration: Different approaches can be followed to use or create new VCF
workflows in existing or new applications:
o API Integration: an API consists of a series of calls that enable to invoke
or start remotely VCF sessions or related functionality.
o SDK Integration: a SDK is a set of libraries, tools, examples and
documentation that can be used by developers to create custom VCFenabled applications.
Figure 42 Review of VCF tools [GASPARINI2013]
Regarding products and companies working on VCF, we find an important online
source on [WEBRTCINDEX.COM]. This website provides a freely available index of
WebRTC vendors and services. Focusing on VCF solutions that expose APIs or SDKs,
the following table summarizes several significant initiatives:
Product/company
Logo/website
Kandy is a real time communications software
development platform, built from. It is a PaaS solution
that includes APIs, SDKs and quick starts (pre-built
https://www.kandy.io/
applications like video shopping assistance).
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Kandy has been developed by GENBAND, a company
that provides IP-based real time communications software
https://www.genband.com/
solutions and products for fixed wireline, mobile, and
cable service providers as well as large enterprises.
Respoke is a communications platform built for web and
mobile developers. It provides simple APIs, libraries and
SDKs abstract the complexities of real-time
communications, allowing developers to focus on user https://www.respoke.io/
experience and business logic.
Licode is an audio/video conferencing solution. It is
based on WebRTC technologies. It allows developer to
include VCF rooms on web applications by means of
JavaScript APIs. It provides a media server which allows http://lynckia.com/licode/
media streaming and recording.
Lynckia is an open source project that provides
conference rooms where participants are able to share
their video, audio and data streams.
Jitsi is an audio/video Internet phone and instant
messenger written in Java. It supports some of the most
popular instant messaging and telephony protocols such
as SIP, Jabber/XMPP, AIM, ICQ, MSN, Yahoo!
Messenger. It is based on the OSGi architecture using the
Apache Felix implementation.
Janus is an open source web conferencing and
collaboration platform. It is a WebRTC general purpose
gateway, providing capabilities to set up media
communication with browsers and relaying on RTP/RTCP
between browsers and the server-side application.
http://lynckia.com/
https://jitsi.org/
http://www.meetecho.com/
https://janus.conf.meetecho.com/
Janus has been created by Meteecho, an academic spinoff of the University of Napoli.
5.7.2 NUBOMEDIA approach beyond SotA
The cloud videoconferencing APIs proposed in the context of NUBOMEDIA, mainly
the Tree and Room APIs, provide powerful tools for developers to build complex VCF
applications in an easy way. These APIs can be seen as high-levels services (based on
the client-server architecture) implementing common VCF topologies, i.e. the Room
(full-duplex real time communication among several participants) and Tree (real time
broadcasting from one presenter to a large number of viewers). Both APIs relies on
WebRTC as media transport, which is provided by other low-level NUBOMEDIA API
such as the Signaling and Media API. These APIs have the following differential
characteristics:
• Server-side and client-side APIs enabling to fully customize the logic of the
application both at client and at application server.
• Integration with advanced media processing capabilities.
• Integration with legacy RTP systems
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5.7.3 NUBOMEDIA outcomes
The main outcomes of the project are the Room and Tree APIs, that enable respectively
to create videoconferencing services based on room and tree topologies. At the time of
this writing, these APIs are on their first versions of development and provide basic
features.
During the third year of the project, further enhancements are planned so that,
depending on the requirements being issued by the industrial partners of the project, the
following features may be implemented:
• Screen sharing
• Transparent simulcast
• VP9 support leveraging SVC features.
• Dominant speaker detection
• Recording
These APIs have been integrated as part of the Kurento Open Source Software project,
and shall be exploited in the context of the NUBOMEDIA community.
5.7.4 References
[RODRIGUEZ2009] Rodríguez, Pedro, et al. "Vaas: Videoconference as a
service." Collaborative Computing: Networking, Applications and Worksharing, 2009.
CollaborateCom 2009. 5th International Conference on. IEEE, 2009.
[GASPARINI2013] Gasparini, Claudio D., et al. "Videoconferencing in eHealth:
Requirements, integration and workflow." e-Health Networking, Applications &
Services (Healthcom), 2013 IEEE 15th International Conference on. IEEE, 2013.
[SKYPE] http://www.skype.com
[WEBEX] http://www.webex.com
[HANGOUTS] https://hangouts.google.com
[VIDYO] http://www.vidyo.com
[VITAM] http://vitam.udg.edu
[OPENMEETINGS] http://openmeetings.apache.org
[OPENTOK] http://www.tokbox.com/opentok
[VSEE] http://vsee.com
[WEBRTCINDEX.COM] https://webrtcindex.com
5.8 Enhancing real-time media developer efficiency
In the past 2-3 years there has been a growing movement and a proliferation of
development environments and frameworks in an attempt to enhance the productivity of
developers. The number of new programming languages and data management
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technologies has exploded and we have passed from development environments lacking
in user-friendliness and isolated to a large extent towards more integrated tools that
provide almost everything. In fact the mobile-first trend has taken development by
storm and has created needs of its own.
To reach a coherent analysis we have focused on technologies present in the
NUBOMEDIA project, and more specifically on those with support for WebRTC.
There are limited resources which provide some kind of compilation of developers
involved in this area. We have explored WebRTCHacks [WEBRTCHACKS.COM] (a
bit outdated, the last review is from 2013) and WebRTCWorld
[WEBRTCWORLD.COM], as they perform regularly a survey of developers active on
this topic.
Additionally we have reviewed [DEVELOPERECONOMICS.COM], a well-known
report on the state of development tools (platforms, APIs, segments, …). In recent years
more focus has shifted towards mobile applications and their particular needs. One
feature of this report is that it does not contain a single reference to real-time
environments, so it does not contemplate this as an area of interest or even a category
worth being mentioned. One conclusion is that they may consider the real-time arena as
something marginal, which is in sharp contrast with the investments in and around
WebRTC players worldwide.
5.8.1 Description of current SoTA
From the review performed we did find that vendors in the WebRTC arena limit their
contribution to developers merely to downloading SDKs or JavaScript libraries, so they
can be included in their typical development environments. We include here only those
with a specific orientation to real-time media developers. We have left out those
offering merely a platform, with no support for developers, or vendors offering for-fee
development services.
In recent years there has been an emphasis on the creation of full development
platforms encompassing all that is needed to build and operate scalable environments
(the DevOps trend is just one example of this). Integration of different technologies is
facilitated by the abundance of API-based development as well as the prevalent RESTmediated communications.
Development around real-time media, and more precisely WebRTC is fragmented, not
very consistent and most times consists of assorted libraries to be integrated into larger
collections of code using traditional tools, in some cases simple editors with no tools to
ease developer’s work.
None of the vendors reviewed provides a “composition” tool to ease the work of the
developer. We have summarized the information in an easy to read format, highlighting
special features where appropriate.
•
•
•
•
AddLive (now a SnapChat company): provides developers with an API that
allows to add live video and voice to applications.
Apizee: provides the webRTC API to simplify the integration of voice and video
chat in web applications.
Bistri: is an open video chat platform, based on WebRTC. It is a PaaS offering
and provides a JavaScript API as well as SDKs for Android and iOS.
Bit6: allows developers to integrate real-time communications into mobile and
web applications. The platform is offered as software-as-a-service and allows
developers to add calling, messaging and many other communication features
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•
•
•
•
•
•
with just a few lines of code. There are SDKs for Android, iOS and JavaScript,
and a plugin in case of using Cordova.
Frozen Mountain Software: With a history dating back 5 years Frozen has
been involved in real-time communications software platforms and SDKs, and
now has a full P2P platform, including full support for WebRTC
communication. The collection of SDKs available is the largest of the
companies reviewed, and they include iOS, Android, several browsers, and
several operating systems (Linux, Windows, Mac). It builds on top of the
Xamarin platform for development (UI- based IDE).
Prologic Technologies: uses TokBox as a building block. They have created
webRTC-based solutions for domains like Health Care/ Tele Medicine, Virtual
Interviews, Skill training of local Craftsmen, or Online Recruitment. All
development support is delegated to TokBox’ OpenTok platform, so it presents
the client-side libraries (JavaScript, Android, and iOS), and the server-side
SDKs (Java, PHP, Python, Ruby and Node.js).
PubNub: runs a globally distributed “Real-Time Network”, a cloud service that
developers use to build and scale large real-time apps, without worrying about
infrastructure. The WebRTC SDK allows developers to use PubNub for
signaling, and enhance their WebRTC apps with features like Presence and
Storage & Playback. They have a JavaScript SDK for WebRTC, but there have
been no new versions in the last 2 years.
Sinch: provides SDKs for iOS, Android, JavaScript and REST APIs to build
applications for voice and video chat. According to their information only the
video component uses WebRTC.
Tokbox: The OpenTok platform allows developers to integrate live, face-to-face
video directly into their website, iOS, and Android apps using WebRTC.
OpenTok provides all the development needs from the infrastructure side
(scaling, stream quality optimization, or even analytics).
NOTE: Since OpenTok has been around for some time and has reached a
satisfactory level of stability, it is the platform being used by several vendors
(according to the information in [WEBRTCWORLD.COM].
Zingaya: Developers can use VoxImplant as a cloud platform to embed realtime communications into any web or mobile application including already
existing one without any additional infrastructure. VoxImplant, developers can
build enhanced communication applications and integrate them with existing
web or mobile services a very short time, thus reducing development costs.
Zengaya’s VoxImplant is limited to voice and video communication. It provides
SDKs for Android, iOS and JavaScript.
Summary:
• Support for developers in most of the companies reviewed above is limited to
providing APIs (mainly JavaScript) and SDKs (iOS, Android, Java, …) and using
REST Web Services for integration.
• There is no unified vision on features or deployment or integration with other
“legacy” systems.
• Integration of code is done at the text editor level (importing/including libraries).
• Lack of visual GUI type of development.
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• Limited applications scope: video/voice conferencing, healthcare, training, one-way
broadcasting… (no not taking advantage of other areas of applications:
security/surveillance, entertainment – music, games, …)
5.8.2 NUBOMEDIA approach beyond SotA
In NUBOMEDIA we are aware that visual development GUIs are not very useful for
creating complete and fully functional real-world applications and that they only
improve efficiency in specific development tasks, mainly GUI composition.
NUBOMEDIA will provide a simple yet functional visual component GUI tool with the
primary purpose of helping developers learn about the NUBOMEDIA APIs and
experimenting with these APIs. For instance, a developer may wish to have a first
contact with the platform, or, after gaining some experience, this same developer wants
to test what a specific filter or module can do, in this case he will use the
NUBOMEDIA GUI tool to create an application in a matter of minutes.
As no other development frameworks explored provide this type of facility, the main
progress beyond the SotA is a reduction in the learning curve of the NUBOMEDIA
media APIs and to enable non-expert developers to understand what the platform
provides, and consequently a faster return on the productivity of the developer. Since
the tool is available as a web application running on the NUBOMEDIA PaaS, there is
no need to install anything, so the developer can get started with no further delay and
get to try and test as much as he wishes. As stated we have not found something similar
in the frameworks explored and this provides an increase in productivity on the
developer’s part.
5.8.3 NUBOMEDIA outcomes
ZED’s main outcome from NUBOMEDIA in this particular topic is a GUI tool that will
simplify part of the game creation lifecycle. During the development of a game several
different types of tools come into action, and for the specific case of multiplayer games,
infrastructure definition takes a lot of time, both in the conceptual phase and the server
definition phase. The tool coming out from NUBOMEDIA, with the visual composition
capabilities will boost the overall development team productivity, since that phase could
be shortened and then made available for immediate testing. This results in reducing
both time and costs, and consequently also provides ZED with a competitive advantage,
by including this tool (with future evolutions and enhancements) into the game creation
lifecycle.
5.8.4 References
[DEVELOPERECONOMICS.COM] https://www.developereconomics.com/
[WEBRTCWORLD.COM] http://www.webrtcworld.com/webrtc-list.aspx
[WEBRTCHACKS.COM] https://webrtchacks.com/vendor-directory/
[ADDLIVE] http://www.addlive.com/
[APIZEE] https://apizee.com/
[BISTRI] http://developers.bistri.com/
[BIT6] http://bit6.com/
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[FROZEN MOUNTAIN SOFTWARE] http://www.frozenmountain.com/
[PROLOGIC TECHNOLOGIES] http://www.prologic-technologies.com/
[PUBNUB] https://www.pubnub.com/
[SINCH] https://www.sinch.com/
[TOKBOX] https://tokbox.com/developer/
[ZINGAYA] http://voximplant.com/
[XAMARIN.COM] https://xamarin.com/
[TOKBOX.COM] https://tokbox.com/developer/
6 Real-time media in vertical segments
6.1 Real-time media for video surveillance and security
In this section we are going to review the state of the art of real time media for video
surveillance and security. We are going to start with a definition of Video Surveillance.
According to Wikipedia, Video surveillance implies the use of video cameras to
transmit a signal to a specific place, on a limited set of monitors. Though all video
cameras fit this definition, the term is most often applied to those used for surveillance
in areas that may need monitoring.
The main use of video surveillance cameras has to do with crime prevention. However,
video-surveillance systems can be used in different locations to fulfill different goals.
An interesting segmentation of the system type is provided by IMS research [IMS] by a
segmentation of the video-surveillance market by industry segments until 2014 (see
figure below).
Figure 43: Video Surveillance industry segments
In the previous figure we can see a very important development of the market (between
2009 and 2014, especially in transportation systems, government and commercial. The
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video-surveillance systems in the IMS-identified industry segments appear to be very
different:
•
Commercial, Retail, Banking and Finance video-surveillance systems are most
of time privately owned and privately operated. Their goals are to protect goods
rather than persons. The data produced by these systems may be used to
prosecute persons (customers, employees..).
•
Government systems are mostly dedicated to the surveillance and protection of
goods and persons within critical infrastructures (ministry premises, nuclear
facilities..). They are operated most of time by public forces, in deep relation
with access control and intrusion detection systems. This segment also embeds
urban-security systems dedicated to the surveillance of public spaces in order to
ensure citizens and public goods protection (against volunteer actions, natural
disasters, accidents and so on)
•
Transportation systems, which do represent an important part of the videosurveillance market, are often dual-use systems. The typical infrastructures
supervised are metro, main lines (stations and on-board) and airports. On the one
hand, the video-surveillance system is used in conjunction with the operation of
the system or the infrastructure (surveillance of a train position in a station, of
the state of electromechanical devices, of queue length in airports and so on).
The operation is for this use performed by private operators belonging most of
the time to the organization responsible for the operation of the infrastructure.
On the other hand, a police use of the video-surveillance system is often
performed for protection of goods and persons. For example, the Paris metro
network is equipped with thousands of video-cameras, used both by RATP
operators (RATP is the operator of the Paris metro), and French Police, but in 2
separated supervision rooms.
After presenting an overview of the different segments, where video surveillance
system are more used, we are going to differentiate the two main systems or architecture
for this kind systems. The main differences between these systems are the cameras used
to record video. These cameras can be analog or IP.
In the traditional analog video surveillance system, security cameras capture an analog
video signal and transfer that signal over coax cable to the Digital Video Recorder
(DVR). Each camera may be powered by plugging in the power supply right at the
camera or by using RG59 Siamese cable which bundles the video and the power cables.
The DVR converts the analog signal to digital, compresses it, and then stores it on a
hard drive for later retrieval. Intelligence is built into the DVR to handle such things as
scheduling, and digital zoom. Monitors for viewing the video are connected to the
DVR, or it can be set up to publish over an internal network for viewing on PCs. The
DVR can also be set up to broadcast over the Internet and can add password protection
and other features. When broadcasting over the Internet, the video for all of the cameras
is transmitted as one stream (one IP address). Therefore, it is very efficient. In the
following figure, we can see an example of the architecture for analog video
surveillance systems.
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Figure 44: Analog Video Surveillance System Architecture
In the IP world, each network camera captures an analog image but immediately
converts it to digital inside the camera. Some digital processing can happen right at the
camera, such as compression. The digital video stream is then broadcast over the local
area network (LAN) using Ethernet (CAT5 or CAT6) cable. Power is supplied to the
cameras through the Ethernet cable via Power-Over-Ethernet (POE) adapters built into
the cameras and at the (POE enabled) switch. The Ethernet cable for each camera is
plugged into the switch which feeds into the network hub. As with all network devices,
some set-up needs to be done for each network camera to set up its IP address and other
identifying attributes.
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Figure 45: IP Video Surveillance System Architecture
A Network Video Recorder (NVR) performs the same function as its DVR cousin in the
analog world. It captures each camera's signal, compresses, and records it. The main
difference is that the video feeds are digital (and much higher resolution, you can see
the difference produced by the resolution between and analog and IP camera in the
following figure) and not analog. Software built into the NVR provides features such as
intelligent search and zoom, etc. The NVR combines the video streams from the
cameras and handles the broadcast over the LAN and internet for local and remote
viewing.
Figure 46: Analog vs IP cameras
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The IP systems under the right bandwidth conditions in a LAN will not have latency
higher than 500 milliseconds. In the same way, and always with the right bandwidth
conditions, if a user attempts to access the system from outside the LAN, the latency
would increase approximately 100 ms.
A part from that kind of systems, companies are appearing now doing video recordings,
and in some cases video processing with different algorithms, in the cloud, using for
this the cloud providers which have been seen on section 5.4.1. Such systems are known
as Video Surveillance as a Service (VSaaS). VSaaS is an easy, smarter way to manage
IP cameras. It is a hosted platform, meaning that viewing, recording, playback is all
done via the web. We could say that the difference among VSaaS, IP and analog
systems is the use of a unit to store and process the video, since VSaaS system does this
over the cloud. Therefore, we could also consider that the other two systems can make
video streaming over internet. On the one hand the analog system, although it has to
convert the image from analog to digital introducing greater latency, the remote users
could connect the DVR to see the live video or video storage. On the other hand, the
NVR offers the same features but without the conversion of the image, and therefore
minimize the image delay. Due to the fact that the NUBOMEDIA project is based on
video processing and stream in real time over the cloud, we will focus from now on at
VSaaS.
Figure 47. Video Surveillance as a Service (VSaaS)
It is also important to highlight that video surveillance systems usually have some kind
of smart algorithms, a part from the typical operations over the video streams such as
image resizing, video storage or video compression. Some examples of these smart
algorithms are motion and face detection.
6.1.1 Description of current SoTA
Once we have seen the main segments of video surveillance systems and its main
architectures, we will see on this section difference commercial and FOSS solutions, as
well as the current scientific state of the art.
Commercial solutions
When we talk about commercial solutions in the Video Surveillance area over the
cloud, it is mandatory to start with one of the largest transactions in recent years in the
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video surveillance market. We are talking about the purchase of the Dropcam Company
by Google [GOOGLE] in june 2014 by $555 million. After the company was bought, it
was renamed to Nest [NEST]. This face reinforces one of the theories that we discussed
on section 5.4.1 , by which tech giants bet for computer vision and in this case for the
video surveillance market.
Nest has created a camera which is a WiFi-enabled security camera ($149 or $199,
depending on video quality) that requires little-to-no-effort to maintain. You plug it in,
get it up on your WiFi, and you’re set. If you just want to be able to check in on your
cameras remotely, that’s free through the Nest app. However, if you want Dropcam to
keep an archive of recorded footage on their cloud servers, that’ll cost you anywhere
from $100 per year you get 10-day online video history and for $300 per year that goes
up to 30 days of history. The Nest Cam can capture 720p or 1080p video and connects
to both 2.4GHz and 5GHz WiFi. Previous the purchase by Google, Dropcam used
Amazon web services as its cloud provider. Right now, although we have not found
information, nest cam may be using google cloud instead of amazon. The services
provided are the following:
•
•
•
•
Video History: Nest Cam continuously records and stores 10 or 30 days of
footage.
Alert summary based on motion and audio detection.
Activity zones: Select the area of the images that you care about and get
personalized alerts when something happens there.
Video sequences: Save and downloads video sequences.
Figure 48: Next (left side) and Axis cameras (right side)
Another important company which we are force to comment is Axis [AXIS]. Axis is the
company which sells more IP cameras around the world. They have cameras a wide
portfolio of solutions for small, mid-size and large scale systems. As for cloud
solutions, Axis does not offer a full solution. Their cameras can transmit video over the
network. In addition, they can upload the video to the cloud and stored it using a
specific service provider. Then, the service provider can manage everything related to
the video storage. Another possibility that Axis offers to their clients is the option to
record the video with a network attached storage solution (NAS), through which axis
needs to do the video streaming in order to record the video in some specific server on
the network. Finally, it is important to emphasis that the axis cameras offers the option
to integrate algorithms in their cameras. A good example of this is Visual Tools, which
is a partner of Axis and the NUBOMEDIA project and has developed several
algorithms to be embedded in Axis cameras, such as motion detection and POS (Video
management point of sale).
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A part from Nest and Axis, which can be the more relevant example about VSaaS, there
are a number of companies which also try to take their video surveillance solutions to
the cloud. We are going to see some examples below:
•
CamaraManager [CAMMANAGER] a Panasonic [PANASONIC] company,
offers its own video surveillance solutions on the cloud. The main important
features of this solutions are:
o live video and audio. In addition they offer the possibility to see
multiple camera images, up to 25 different cameras, at the same time.
o Storage: After connecting your camera to the Cameramanager
application, the video immediately & automatically start to be saved to
the cloud. You can choose to store between 24 hours, 7 days or 30 days
and video quality up to Full HD. In addition, this software allows the
user to easily schedule when they want to start recording to the cloudm
choosing between continuous recording, specific days & hours recording
or only record when motion, heat or sound is detected.
o Detection: this application offers the option to record video when
motion, heat and audio is detected. In addition, the user can set specific
detection areas within the images.
o Alarms: when some detection has been captured by the system, the
application can send you a notification to your computer, Smartphone or
tablet.
•
Neovsp [NEOVSP]: The cloud solution is a platform that intelligently stores and
manages video content from IP and analog cameras and efficiently delivers
content to multiple user devices such as web browsers, mobile phones and
media sets. This solution is aimed at Telecommunications companies (telcos),
data centers, ISPs, and cable service providers, so that they can use their global
infrastructure for another value-add service by offering video surveillance
capabilities to their customers on a subscription basis. We highlight the
following features and services about this solutions:
o Scalability: the solution allows for system growth parallel to the growth
of the client base, by adding servers to the cluster used to manage the
video as needed.
o Formats: this solution can broadcast the media in a variety of popular
high-quality formats, such as MPEG-4, 3GPP, H.264 Microsoft
Windows Media (WMV), Apple QuickTime (MOV) and Adobe Flash.
High-Definition (HD) quality is natively supported.
o Multiplatform: this solution is accessible via any web browser,
mobile/smart phones (including Android and iPhone), and tablet PCs
o Storage: video data content is stored in a cluster platform. This platform
stores video data content from unlimited sources, and allows remote
access.
o Video streamed content
o Video Pos integrated solutions
•
Iveda provides a platform called Sentir [SENTIR] for video management with
video streaming and storage technology. It offers the video surveillance
functionality of traditional security industry DVRs (digital video recorders) and
NVRs (network video recorders), all delivered from the cloud as an application.
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A part from the video streaming and storage capabilities, this solution does not
seem to offer more services related to Video Surveillance. Although they offer
the option to back up the video into a slave server. In the same way as the
Neovsp, the Sentir platform is aimed at telcos, data centers and ISPs.
•
Smartvue [SMARTVUE], this company offers different Video Surveillance
service on the cloud:
o CloudVue: Fast and secure remote video management and monitoring
for one to thousands of locations. CloudVue optimizes video delivery for
any bandwidth.
o CloudDrive: On demand cloud video storage service that is secure and
redundant
o MapVue: See surveillance locations on a global map and quickly
monitor video and system status. Use geographic maps populated with
interactive camera icons that show live camera previews when you hover
over them. They are color-coded to notify you of server and camera
statuses
o Other services: A part of these services, they are working in other ones,
such as CommandValue through which the users will be able to get
reports about the cameras and cloud servers, or Contribute which is a
social surveillance on demand that provide to the security staff an
application that allow them to connect to the platform and send the video
through their mobile phones or tablets.
•
Stratocast [STRATOCAST] is another company with offer a video surveillance
cloud solution. They technological partners are Axis which provides them the
cameras and Microsoft Azure as a cloud provider. Though its solution,
Stratocast offers video streaming and storage. Furthermore, they offer intelligent
video management through which the user can monitor multiple cameras, zoomin on points of interest, playback recorded video to find incidents and see key
events. Moreover, they offer an intelligent timeline indicates when activity has
occurred, for example based on motion detection, in order to rapidly search
through your recordings. Finally, they introduce a permanent storage feature for
those video sequences that the users need to maintain saved.
A part from these commercial solutions we can find others which offer solutions with
similar features. Unifore [UNIFORE], SecureWerk [SECUREWERK], Ynetworks
[YNETWORKS]are other examples of companies which also offer video surveillance
solutions on the cloud.
FOSS solutions
When we talk about FOSS solutions in the video surveillance sector over the cloud, we
have found a limited number of platforms. Here, we will talk about three solutions.
Although, only one of these solutions is deployed on the cloud. The other solutions are
open source software in the video surveillance sector, and we have included them on
this part of this deliverable, since they carry out video streaming over the network, for
example for live video.
Possibly the most interesting platform is iSpy [ISPY]. iSpy is an open source Video
surveillance software, which offers the option to deploy its software over the cloud.
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iSpy software is free to use a local level, that is why we also consider free, but when we
want to use the software on the cloud, we need to pay between $8 and $49 to deploy the
product in the specific server over the cloud. The source code is available on github
(https://github.com/ispysoftware/iSpy) . In the same line as the commercial solutions
iSPy provides to the user the following main features:
•
•
•
•
•
•
Access live video and audio.
Record video stream and audio.
Recording schedule
Motion detection
Alerts by email or SMS
Unlimited number of cameras
Figure 49: iSpy FOSS solution
The following FOSS solution is ZoneMinder [ZONEMINDER]. It is a free and open
source Closed-circuit television software application developed for Linux which
supports IP, USB and Analog cameras. This solution does not offer the possibility to
deploy it on the cloud. However, it does offer the remote access to live video and
remote playback of the video storage. Therefore, it performs video streaming over the
network and for this reason has been included on this section. Its source code is
available on github ( https://github.com/ZoneMinder/ZoneMinder). It offers the similar
group of pictures than the other solutions previously seen. These features are:
•
•
•
•
•
•
Video capture
Video recording,
Monitoring and live video
Definition of region of interest within the image
Motion detection
Event notification by email or SMS
The last FOSS platform is OpenVSS [OPENVSS](Open Video surveillance System). It
is free and open source software, and you can find the source code at google code
(https://code.google.com/p/openvss/). The main point to highlight is that as almost all
the projects located at google code, OpenVSS has been archived and it is just available
in read only mode. Another important point is that it does not seem to offer a cloud
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solution. The main features of this platform are video analyzing, recording and
indexing, with searching and playback services. It supports multi-cameras platform,
multi-analyzers module (OpenCV integrated), and multi-cores architecture. The
OpenVSS software consists in three main packages:
•
OpenVSS server: an online video processing system for analyzing, recording
and indexing
o VsMonitor: Video analyzer management.
o VsAdmin: remote connection tool for activating the analyzer, recorder
and alerter.
o VsSystem: system configuration tool.
o VsService: service configuration tool.
•
OpenVSS client: client application
o VsLive: video live views.
o VsPlayback: video searching and playback
•
OpenVSS SDK: additional plug-ins
o VsAnalyzerSDK: analyzer plug-in, source code generator integrated with
OpenCV 2.0
Scientific publications
Researchers are also interesting on deploy video surveillance system on the cloud. If we
loof for the term “Video Surveillance, Cloud Computing” at google scholar, we can
obtain 2.400 results in this year, which is a 20 percent of the result obtained for
computer vision and cloud computing. However, a big amount of the entries
corresponding with this search are only related to cloud computing. Otherwise, if look
for the term “VSaaS” we only get 22 result for 2015. Therefore, we can say that video
surveillance is an area with little interest in the scientific area. Now, we are going to see
some examples.
The paper “The UTOPIA Video Surveillance System Based on Cloud Computing”
[PARK2015] explain how the authors of the publication have created a video
surveillance system prototype based on cloud designed for their smart city called
UTOPIA. In this smart city, there are a lot of networked video cameras to support smart
city services and they generate huge data continuously. It is usually required that the
smart city should process the huge and continuous video data, real big data in real time.
The use of cloud computing is used in the prototype to provide the enough computing
resources to process big data successfully. This system collects big video data through
scalable video streaming which smoothly processes big data traffic from large number
of networked video cameras even with limited bandwidth and process the big data with
MapReduce model. The objective of this paper is to present a new mechanism for the
optimum cloud resources management based on dynamic scheduling policies and
elasticity needs in the video surveillance sector.
Another example is the paper “Framework for a Cloud-Based Multimedia Surveillance
System” [ANWAR2014], through which the authors propose a framework so that
modern multimedia surveillance systems, which comprise a large number of
heterogeneous sensors distributed over multiple places, may overcome their limitations
in terms of scalability, resource utilization, ubiquitous access, searching, processing
and storage. The paper discusses the different design choices. It further proposes a
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general cloud-based surveillance system framework and analyzes it in light of the
different design issues. In the following figure we can see the framework proposed. On
this framework, we can distinguish the multimedia providers (cameras and other
sensors), surveillance users or consumers, the system core and the service stack. In the
system core the publish-subscribe broker is one of the most vital components of the
system, since it is in charge of publishing and subscribing the media streams to the
appropriate clients. The multimedia surveillance service directory makes the system to
adopt a service oriented architecture style and hence all its functionalists are exposed as
services that are accessible over the internet. The cloud manager module is responsible
for managing the cloud based-operations of the proposed framework. It acts as the
bridge between the users and the cloud surveillance system components. The
Monitoring and metering module is responsible for performance monitoring and usage
tracking of cloud virtual machine (VM) resources and provides statistics of usage and
billing. Finally, the resource allocation manager manages and allocates several virtual
machines (VM) resources for running the surveillance system and associated services.
In the service stack the different services of the system are defined. These services
include video processing service, storage service, big data analytics service, payment
service, composition service, media delivery, and security and privacy services.
Figure 50: Framework for a Cloud-Based Multimedia Surveillance system
6.1.2 NUBOMEDIA approach beyond SotA
As we saw in the previous section we have seen that the video surveillance systems are
a very active field for private companies and their business solutions. A good example
which shows that video surveillance is growing sector and has a significant market, it
has been the purchase of Dropcam by Google or the important number of companies
that we can find in this sector. However, we have found little FOSS solutions about
video surveillance system on the cloud. In the same way, scientific publications do not
reach a significant volume.
As for the commercial and FOSS solutions, we have seen on the previous section that
all of them have similar functionalities. These functionalities are:
• Live video
• Video Storage
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•
•
•
Recording schedule
Video Content analysis software as motion detection or POS (points of sale)
Alerts
On this context, the NUBOMEDIA platform will be one of the few FOSS platforms on
the market. Futhermore, NUBOMEDIA presents more advantages over the different
commercial and FOSS solutions:
• The flexibility to create applications composed by different filters in a short
time. This would mean that companies could very easily make more
customizable applications. This outcome is particularly relevant, since we have
not found any similar capability on the open source platforms and applications
found.
• Thanks to NUBOMEDIA the applications in the video surveillance system can
be richer, since it will not only provide basic video analysis operations such as
motion detection, but it also provides more complex video surveillance
operations. In addition, the use of the Augmented Reality capabilities can be a
differentiation factor with the rest of solutions.
• The capability to use filters which have not been found on the solutions studied
and are able to provide a value added to surveillance systems. These filters, at
the time this part of the State of the Art document was written, are perimeter
intrusion detection, face detector and tracking.
• Finally, as NUBOMEDIA is an open source community the number of filters,
from which the developers can take advantage, may increase considerably
generating an important number of filters for this sector.
6.1.3 NUBOMEDIA outcomes
On this section we are going to see the different outcomes of NUBOMEDIA related to
Video Surveillance System. At time of writing this part of the deliverable (end of
November of 2015), the functionalities developed on NUBOMEDIA that can be useful
in the video surveillance sector are:
• Video Streaming
• Video Storage
• Motion detector
• Object Tracker based on motion
• Perimeter intrusion detector
• Augmented reality functionalities, as overlay text on the image
During the next year, a full featured demonstrator leveraging these capabilities for video
surveillance application shall be created.
6.1.4 References
[IMS] https://technology.ihs.com/
[NEST] https://nest.com/camera/meet-nest-cam/
[GOOGLE] www.google.com
[AXIS] http://www.axis.com/
[CAMMANAGER] https://www.cameramanager.com/website/
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[PANASONIC] http://www.panasonic.com
[NEOVSP] http://www.neovsp.com/
[SENTIR] https://www.iveda.com/sentir/
[SMARTVUE] https://www.smartvue.com
[STRATOCAST] http://www.stratocast.com/
[UNIFORE] http://www.unifore.net/
[SECUREWERK] http://www.securewerk.com/
[YNETWORKS] http://ynetworks.in/
[ISPY] https://www.ispyconnect.com/
[ZONEMINDER] https://www.zoneminder.com/
[OPENVSS] https://code.google.com/p/openvss/
[PARK2015] Jong Won Park, et al. " The UTOPIA Video Surveillance System Based
on Cloud Computing " CLOUD COMPUTING 2015: The Sixth International
Conference on Cloud Computing, GRIDs, and Virtualization.
[ANWAR2014] M. Anwar Hossain "Framework for a Cloud-Based Multimedia
Surveillance System", International Journal of Distributed Sensor Networks Volume
2014, Article ID 135257.
6.2 Real-time media for news reporting
6.2.1 Description of current SoTA
Traditional live news and sport coverage from the field, was done using satellite
transmission. Such broadcasting obligated TV station to use complicated, heavy and
unique equipment for their live field reports, such equipment required a satellite dish
usually installed on top of dedicated vehicles. LiveU have revolutionized the live
broadcasting world by using the cellular wide coverage offered today. LiveU “bonding”
approach groups several wireless modems together to allow a wideband and more stable
communication channel for live HD video and audio broadcasting, freeing reporting
teams from awkward heavy equipment while supporting fast deployments, indoor and
mobile transmissions.
Social Networks:
The increasing growth of social networks as modern method for communication on
global scale, making of the web the “global village” which was promised since it was
popularized. Interactive social networks provide a powerful and creative way for
producing multimedia contents, often known as user generated content (UGC). In UGC
regular people/consumers are volunteering their content on a public domain and
typically for free, thus the use of such media by reporters has been in a rapid growth
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during the last years, named “citizen journalism” [gigaom website]. Broadcasters and
media suppliers are looking for ways to handle UGC and citizen journalisms to increase
the liveness, diverseness and richness of their content.
Online Video Platforms:
UGC led a growing market of online video platforms (OVP’s), a service allowing users
to upload, convert and store their media. OVP can come as SaaS or DIY model solution
as well. The vast majority of OVP solutions today use industry-standard HTTP
streaming or HTTP progressive download protocols. With HTTP streaming, the de
facto standard is to use an adaptive streaming where multiple files/chunks of a video are
created at different bit rates, but only one of theme is selected and streamed at a given
time to the end-user during a playback, depending on the available bandwidth or device
CPU constraints. Such service providers for examples are:
• Kaltura
• Ustream
• Live youtube
• Livestream on facebook
• Bambuser
OVP might be a great way for users to share their media, but it is a standalone solution
that can’t provide broadcasters an easy way to access media streams, unless they use it
for their own streams.
Content Delivery Networks:
A common way for publishers to stream their media is by using content delivery
networks (CDN). CDN is a globally distributed network of proxy servers deployed in
multiple data centers. The goal of a CDN is to serve content to end-users with high
availability and high performance. CDNs serve a large fraction of the Internet content
today, including web objects (text, graphics and scripts), downloadable objects (media
files, software, documents), applications (e-commerce, portals), live streaming media,
on-demand streaming media, and social networks.
Poplar solutions are:
• Akami
• Limelight Networks
CDN however are far from answering the needs of broadcasters needs for UGC media
share, they hardly support HD, HQ or live broadcasting. In addition, they are p2p
services that focus mainly on the delivery/transport functions.
Messengers/Streaming Applications:
A common option for broadcasters to connect with users (and a very unprofessional
one) and exchange media is via general purpose applications. Such application, software
and services are spread among users as usually method of communications, making it
accessible for users to use their already installed app and use it to connect broadcasters
much like they would connect their friends and family. For example, on 1st of January
2016 after a terror attack in Tel Aviv, the leading news company in Israel TV channel 2
news broadcast security videos of the attack by steaming it from what’sup.
Another application common for general users used by broadcasters is Skype, used for
interviewing especially when it is not cost effective to send a reporting team.
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General purpose commercial channels although widespread come with their limitations.
For general use the latency, reliability and quality are not as important when
broadcasting live for millions of people at professional TV stations. Of course such
channels provide services for other tens of millions of users sharing resources and
services. In addition, news editors are occupied with workstation, when using
unprofessional channels which do not interface with the workstation, it makes editing
and broadcasting that much harder.
This need encouraged other technological related to the field, designed to take HQ
media sharing into the future.
Compeit
Internet-based distribution will transform media broadcasting towards higher levels of
interactivity and integration with virtual, mixed and augmented reality. It will be
enabled by new web technologies and a proliferation of devices for audio, video and
tangible interaction. COMPEIT creates highly interactive, personalized, shared media
experiences on the Internet for feeling more present when interacting remotely with
other people and enjoying media together.
Cognitus
COGNITUS will deliver innovative ultra-high definition (UHD) broadcasting
technologies that allow the jointly creation of UHD media exploiting the knowledge of
professional producers, the ubiquity of user generated content, and the power of
interactive networked social creativity in a synergetic multimedia production approach.
The project will provide a proof of concept to cement the viability of interactive UHD
content production and exploitation, through use case demonstrators at large events of
converging broadcast and user generated content for interactive UHD services. The
envisaged demonstrators will be based on two different use cases drawn from real-life
events. These use cases are in turn single examples of the fascinating and potentially
unlimited new services that could be unleashed by the un-avoidable confluence of UHD
broadcasting technology and smart social mobile UGC brought by COGNITUS.
Building on recent technological advances, in UHD broadcasting and mobile social
multimedia sharing coupled together with over fifty years of research and development
in multimedia systems technology, mean that the time is now ripe for integrating
research outputs towards solutions that support high-quality, user sourced, on demand
media to enrich the conventional broadcasting experience. COGNITUS vision is to
deliver a compelling proof of concept for the validity, effectiveness and innovative
power of this integrated approach. As a consequence, over 36 months the project will
demonstrate the ability to bring a new range of dedicated media services into the
European broadcasting sector, adding critical value to both media and creativity sectors.
6.2.2 NUBOMEDIA approach beyond SotA
From the broadcasters point of view, current off the shelf solutions for UGC media
sharing with broadcasters such social sites and messenger services (i.e. Facebook, and
Whatsapp as examples), are enabling content sharing in live and archived manners,
however they have a several disadvantages that we are aiming to advance with the
“news room” service:
- Exporting live video from existing solutions is not always supported
- Creating large groups (for example in Whatsapp) is usually not supported
- The broadcasters need to develop specific tools to connect to each service
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-
They are not often provided and not allow easy access to important information, for
instance, knowing of the exact location of the UGC contributors.
They are not designed with the exact specifications of the broadcasters, rather they
are designed for unprofessional use cases, thus the quality of the stream to the
broadcaster does not have any priority over normal users of the service.
The News Room, service will overcome these limitations, providing a specific designed
service for the broadcasters.
From the new media service providers’ point of view, they would like to engage with as
many as broadcasters they could around the globe, while using the
NUBOMEDIA TV Broadcasting Connector (TBC) the main novelty is that it will offer
easy way for them to connect their UGC users with LiveU central/Community and to a
list of over 1000 (and growing) broadcasters. In addition, the TBC use of WebRTC
technology will allow simple web based social services and their users to stream and
receive live video directly from their browsers while the service is engaged with the
Broadcasters via the NUBOMEDIA connector concept. The TBC will support
simultaneously P2MP distribution with low delays of WebRTC streams which will
allow easy web based interactive services while saves critical development time to the
developers.
To summarize, using NUBOMEDIA technology the following main novelties are
achieved:
•
•
A UGC service provider could easily connect its users via the TBC connector to
thousands of TV broadcasters.
The Broadcasters News Room service, is a specific designed service for
broadcasters to engage users with their in-house systems.
6.2.3 NUBOMEDIA outcomes
The designed service using NUBOMEDIA technology should revolutions the way
broadcasters retrieve media and broadcast it to their customers. The mentioned services
could replace the current methods of communication to broadcasters and fill the gap of
current solutions.
Compared to OVP, NUBOMEDIA will allow easy development environment to UGC
service providers who wants to integrate with Broadcasters.
Compared to CDN, NUBOMEDIA WebRTC integrated with LiveU Multimedia Hubs
support p2mp, live, HD and HQ streaming in a reliable manner.
Compared to general purpose application, NUBOMEDIA and LiveU demo/usecase
using WebRTC input streams and point to multi point (P2MP) technology, will provide
a dedicated purpose custom software application to connect the broadcasters with UGC
at any events providing richness and liveness of content.
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NUBOMEDIA outcomes, is the base for technology, using D2.4.2 TV Broadcasting
connector such a service of connecting users and broadcasting news groups with an
infrastructure for the mentioned “News Room”. This virtual room will provide direct
means of connecting broadcasters and media suppliers. Allowing for both professional
and amateur reporters to provide quality live feeds of broadcast from every point
covered with cell reception. This services are a part of the designated “BeFirst” solution
will encourage citizen journalism [Goode, L., 2009, Bruns, A. (2010)], which is
characteristic way of media consumption in the information era. The increasing
consumption of citizen journalism will drive this NUBOMEDIA depended service to
come together in the near future.
6.2.4 References
[compeit.eu ] http://www.compeit.eu/
[spl.edu.gr/index.php/projects/active-projects/cognitus/]
http://www.spl.edu.gr/index.php/projects/active-projects/cognitus/
[gigaom.com/2015/12/28/this-was-the-year-social-networks-turned-into-newsorganizations/]
https://gigaom.com/2015/12/28/this-was-the-year-social-networksturned-into-news-organizations/
[Krumm2008] Krumm, John, Nigel Davies, and Chandra Narayanaswami. "User-generated
content." IEEE Pervasive Computing 4 (2008): 10-11.
6.3 Real-time media on e-health environments
The healthcare industry has been attempting to find new models of care delivery as a
response to the escalating cost of care, shortage of medical professionals, increased
number of patients with chronic diseases and inefficiencies in the current system. To
that aim, e-health (or telehealth) is emerging as one of the innovations in healthcare
industry to address the challenges of access, experience and efficiencies. E-health
services are delivered online through the Internet such as via e-mail, web applications
and videoconferencing. Moreover, currently novel augmented reality (AR) approaches
are more and more adopted in e-health by practitioners. All in all, the current
description of the state-of-the-art on e-health environments is divided into three parts:
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1. Videoconferencing used in e- health environments.
2. Instant messaging communications for e-health.
3. Augmented reality technologies on e-health communications.
The following section presents a summary of the most significant current research
efforts in these areas. In addition, in order to complete this study, relevant companies,
products, and technological solutions are also presented.
6.3.1 Description of current SoTA
Videoconferencing used in e-health environments
Video based telehealth is emerging as an important technology for effective and
efficient collaboration between providers and patients. It facilitates face to face real time
interactions with the specialists exchanging patient data resulting in prompt feedback.
The feedback is crucial for overcoming time and spatial limitation for interactive telelearning and quick knowledge sharing in order to provide better local treatment to
patients [NILSEN2012].
In the case of an on-demand appointment model, the selection of the provider can be
initiated by the patient or by the system. For example, a patient can click on a button on
a mobile app, make a payment and the system can route the call to an available
provider. While these models provide convenience, the fact that the patient has not
interacted with the provider can result in a trust issue [LIU2008].
[VARGHEESE2014] proposes a model takes the validation further and provides an
enhanced trust model leveraging in-session real time video real time analytics. The key
benefits of that approach are improved patient satisfaction, increased trust on behalf of
the patients and enhanced compliance.
To ensure patient confidentiality, national regulations exist to address control of access
and transmission of medical data with which telemedicine systems may have to comply.
In terms of encryption of the transmitted data, the most usual solution is to use secure
socket layer (SSL) or transport layer security (TLS) with a hypertext transfer protocol
secure (HTTPS) web address [SCHREIER2008]. An alternative is to use a virtual
private network (VPN) which effectively brings a remote user into the secure private
clinic intranet over an insecure Internet link by a secure connection [FENSLI2005]. For
mobile devices, an additional preliminary safeguard before making such a secure
connection, or controlling access to authorized users only beyond login details (e.g.,
username, password), is to verify the device used to access the data. The knowledge a
mobile network operator has regarding its subscriber is used to verify the subscriber
identity module (SIM) card to the clinic for data access [LIU2008b]. Once a connection
is established and before data is transmitted, some additional mutual authentication
between people at the remote ends is another safeguard proposed [YAN2010]. Here
biometric data, such as from face recognition, was proposed to verify a clinician’s
identity to remote users.
Research for applications in low-bandwidth connectivity was identified [STEELE2013].
This work considers the use of new forms of health consumer and public health
communication via health social networks, the use of advanced and automated health
emergency notification systems including smartphone-based systems, the use of
smartphone-enabled sensor technologies for home-based capturing of physiological data
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for at-risk patients, the use of personal health records by chronically ill and emerging
mobile device–based videoconferencing capabilities.
[CLARKE2008] conducted a systematic review of the literature to critically analyze the
evaluation and assessment frameworks that have been applied to telemedicine systems.
It presents a framework that has been used to evaluate videoconferencing systems
telemedicine applications. The conclusion of this work is that that there has been
insufficient rigorous objective technical evaluation and assessment of telemedicine
systems.
[KIAH2014] proposes a secure framework for video conferencing systems, using JMF
(Java Media Framework) to develop secure framework and the RTP over UDP protocol
to transmit the audio and video streams. This work uses the RSA and AES algorithms to
provide the required security services.
Real-time tele-echocardiography is widely used to remotely diagnose or exclude
congenital heart defects. Cost effective technical implementation is realized using lowbandwidth transmission systems and lossy compression (videoconferencing) schemes.
[MOORE2008] uses DICOM (Digital Imaging and Communication in Medicine) video
sequences were converted to common multimedia formats, which were then,
compressed using three lossy compression algorithms. We then applied a digital
(multimedia) video quality metric (VQM) to determine objectively a value for
degradation due to compression.
Regarding telerehabilitation, services fall into two categories: clinical assessment (the
patient’s functional abilities in his/ her environment) and clinical therapy. To provide
both types of services remotely while interacting with the patient, the rehabilitation
professionals rely on establishing a telepresence through bidirectional video and audio
from videoconferencing equipment connected through a high-speed Internet connection.
[HAMEL2008] proposes a telerehabilitation platform consisting of two H264
videoconferencing codecs (Tandberg 500 MXP) with integrated wide-angle view
cameras and remotely controlled pan tilt zoom (PTZ) functions, local and remote
computers with dedicated modular software interfaces for user-friendly control of
videoconferencing connections, PTZ camera function, and external devices (i.e., tablet
PC and sensors).
In [HERNANDEZ2001], the authors proposed a tele-homecare multimedia platform for
videoconferencing based on standard H.320 and H.323, and a standard TV set based on
integrated services digital network (ISDN) and Internet protocol (IP) to let the patients
upload their physiological information to a healthcare center and to provide telehomecare service such as consultancy over phone.
The following table summarizes some of the main companies and their products in the
domain of e-Health videoconferencing:
Lifesize - https://www.lifesize.com/
Lifesize, a division of Logitech, provides high definition videoconferencing endpoints and accessories,
and infrastructure services. Some of the main products of Lifesize are the following:
-
Lifesize Cloud, which is a desktop/mobile video conferencing application
o https://www.lifesize.com/en/solutions/cloud
Lifesize Icon Series, which is video conference application
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o https://www.lifesize.com/en/products/video-conferencing-systems/icon
Lifesize Phone HD, ut is a smartphone for your conference room—everything that you need to
make web, audio and video conference calls.
o https://www.lifesize.com/en/products/video-conferencing-accessories/phones/phonehd
Digital Resources Inc. - http://www.digitalresources.com/
DRI is an equipment provider and systems integrator. They provide services from visualization to telepresence, from acquisition to post production, from streaming to IPTV to broadcast. Regarding
telemedicine, provides services based on three kinds of videoconferencing systems
(http://www.digitalresources.com/medical.aspx):
-
Standard Videoconferencing systems, for use in the doctor’s office or for medical education,
Purpose-built telemedicine carts equipped with videoconferencing capabilities, and
Unmanned robotic systems or any number of medical devices such as a stethoscope or blood
pressure monitor.
Vidyo Inc. - http://www.vidyo.com/
Vidyo provides both software-based technology and product-based visual communication solutions. The
company’s VidyoConferencing solutions take advantage of the H.264 standard for video compression.
The portfolio of Vidyo includes the following products (http://www.vidyo.com/products/):
-
VidyoRouter: Media routing and video optimization
VidyoPortal: Management and call control
VidyoGateway: Interface for H.323 and SIP
VidyoReplay: Conference recording and webcasting
Vidyo Server for Microsoft Lync: Native integration with Microsoft Lync
Vidyo Server for WebRTC: Native integration with WebRTC
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IVèS - http://www.ives.fr/
IVèS stands for Interactivity Video and Systems. This company develops, integrates and deploys
innovative services in the fields of e-health, videoconferencing and relay centers for the deaf and hard of
hearing people. Their products includes solutions for (http://www.ives.fr/index.php/en/ives/ives-thecompany/11-content-uk):
-
-
Videoconference: Meeting rooms equipped with dedicated terminals with flat screens and
motorized cameras can connect multiple sites together. These high-end solutions provide HD
video and high quality of sound with adapted microphones and speakers.
Webconference: allows with equipment such as PC, Mac, mobile, tablet, videophone or phone
to do a conference over the Internet.
Polycom - http://www.polycom.com/
Polycom is a multinational corporation that develops video, voice and content collaboration and
communication technology. The company licenses H.264 video codecs, Siren codecs, session initiation
protocol, native 1080p high-definition cameras and displays, native 720p and 1080p high-definition
encoding/decoding, low-latency architecture and low bandwidth utilization, wideband advanced audio
coding with low delay (AAC-LD), multichannel spatial audio with echo cancellation and interference
filters to eliminate feedback from mobile devices, and inter-operation with legacy video conferencing.
Regarding telemedicine and telehealth, the main products of Polycom are listed as follows
(http://www.polycom.com/solutions/solutions-by-industry/healthcare/telemedicine-telehealth.html):
-
-
Polycom RealPresence Platform: collaborative infrastructure that enables healthcare
professionals and patients to communicate.
Polycom Practitioner Cart: brings high-definition video, audio and image-sharing to medical
professionals and patients no matter where they are located.
Polycom HDX 6000-View Media Center: compact solution for a patient or small group of
patients to collaborate with a far end practitioner or to participate in a video call with other
groups of patients at a distance.
Polycom CMA Desktop and HDX 4500: Telemedicine video call solution. The practitioner can
place a video call and control the far end camera to get a good look at their remote patient.
Video Content Management: Software for secure video capture (recording and playback), as
well as content management, administration, and delivery of video content.
RealPresence Mobile Polycom: video client application delivers HD-quality, audio, and content
sharing to tablet users.
Table 10 Companies and products related with e-Health videoconferencing
Instant messaging communications for e-health
Smartphones support several means of communication including voice calling, video
calling, text messaging, email messaging, multimedia (text, image, and video)
messaging, and conferencing through the cellular phone service provider [MOSA2012].
Besides standard communications, clinical communication applications are designed to
simplify communication among clinicians within a hospital.
[GAMBLE2009] describes mVisum, a communication application for cardiologist’s
that receive patient data on smartphone. It receives monitor data, alarms, lab results,
echocardiograms, discharge notes and other reports. This app is available for iPhone,
Android, Windows Mobile, and Blackberry.
Other mobile applications used for instant messages are listed in the following table (see
http://www.ehealth.acrrm.org.au/technology-directory):
App name
Description
Global Meet (mobile)
Price: Free App
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LifeSize Clear Sea
Polycom RealPresence Mobile
Scopia Mobile
Skype (mobile)
Operating system compatibility: iOS, Blackberry
Features: Call recording
Price: Free App
Operating system compatibility: iOS, Android
Frames per second: 30fps
Features: Call recording
Compatibility: QLD Health
Price: Free trial
Hardware standards: H.239, H.263+, H.264, H.323 H.460
(Firewall traversal)
Operating system compatibility: iOS, Android
Price: Free App
Operating system compatibility: iOS
Price: Free App, free calls, free video calls
Operating system compatibility: iOS, Android
Features: Encryption
Table 11 Mobile applications that can be used for e-Health instant messages
Augmented reality technologies on e-health communications
Augmented Reality (AR) refers to a live view of physical real world environment whose
elements are merged with augmented computer-generated images creating a mixed
reality [BEHRINGER2007]. There are several domains in which AR can be used for ehealth.
AR technology provides the medical field with possibilities for improvements in
diagnosis and treatment of patients. This technology offers patients and health
professionals with potential benefits in therapy, rehabilitation, diagnosis, and
explanation. [GARCIA2014] proposed an immersive AR system to create multiple
interactive virtual environments that can be used in Parkinson Disease rehabilitation
programs. The main objective of this work is to develop a wearable tangible AR
environment focused on providing the sense of presence required to effectively immerse
patients so that they are able to perform different tasks in context-specific scenarios.
The findings of this work help to evaluate the viability of using AR as an auxiliary tool
for Parkinson Disease rehabilitation programs.
The ability of augmented reality to combine three-dimensional scan data with real-time
patient images has made it invaluable in the medical industry. It has enormous potential
in radiation oncology [NICOLAU2005].
Another hot topic related with AR is the healthcare education. A possible approach to
provide learning opportunities in this area is the use AR where virtual learning
experiences can be embedded in a real physical context. [ZHU2014] provides a
complete integrative review of this domain.
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SCOPIS - http://www.scopis.com/
SCOPIS is an example of augmenting the endoscopic video data with 3D planning information,
registered with the 3D intraoperative anatomy of the patient.
Brainlab - https://www.brainlab.com/de/chirurgie-produkte/ubersicht-uber-neurochirurgieprodukte/mikroskopintegration/
Brainlab have started integrating AR supported navigation systems into optical see-through devices. In
order to take full advantage of augmented microscopic views a video see-through device would be a
much better platform for augmented reality based navigation software (e.g. registration, synchronization,
image composition).
Arri Medical - http://www.arrimedical.com/
The company ARRI has released the first video see-through operating microscope. The digitalization of
the real world with high quality stereo cameras and optics in combination with surgical applications
being characterized by complex anatomical structures, availability of 3D imaging data, preoperative
planning procedures can be a perfect match to develop Augmented Reality software solutions that bring
a real benefit for patient treatment.
Google Glass - http://www.google.com/glass/start/
Google Glass is a headset, or optical head-mounted display, that is worn like a pair of eyeglasses. Its first
version is maybe not the very best hardware solution to make the difference and convince non-believers
about the potentials of AR, receiving massive criticism and legislative action due to privacy and safety
concerns.
Microsoft Kinet - https://dev.windows.com/en-us/kinect
Microsoft came up with the Kinect sensor, which allowed to combine tracking information with video
data to create AR scenes. Among other ideas how to use this sensor, people started developing AR
mirror interfaces for multiple applications, e.g. an anatomy mirror.
Oculus Rift - https://www.oculus.com/
On March 2014 Facebook bought Oculus Rift, a Virtual Reality interface. The Rift is a virtual reality
head-mounted display developed by Oculus VR. Zuckerberg said: “At this point we feel we’re in a
position where we can start focusing on what platforms will come next to enable even more useful,
entertaining and personal experiences.”
Table 12 Companies and products related with e-Health AR
Finally, several research efforts have been done in the medical imaging and imageguided surgery. The work presented in [NAVRATIL2011] describes a prototype of realtime stereoscopic transmissions of robotic surgeries using a system for low latency
streaming over packet networks. Medical AR systems can merge virtual images into
real surgical scenes. Examples include AR microscope, projection AR system, head
mounted display and image overlay [LIAO2011].
AR is still considered as a novelty in the literature. Most of the studies reported early
prototypes. For that reason, there are not yet many professional solutions available on
the market. Nevertheless, there are some companies that are working on create products
and solutions in this domain. A good reference that summarizes these efforts is
available online on http://medicalaugmentedreality.com/. The following table
summarizes several significant companies and products:
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6.3.2 NUBOMEDIA approach beyond SotA
The environment of ICT applied to e-health is a multi-billion market with very relevant
stakeholders in hard competition. In this context, the NUBOMEDIA approach beyond
SotA is pragmatic and aims at defeating some barriers and limitations shared by most of
the above mentioned solutions. In particular, these include:
• Most solutions suitable for e-health communications (i.e. providing the
appropriate reliability and security guarantees) are extremely expensive and not
affordable for many organizations even in developed countries.
• None of the described solutions combines together interoperable real-time
media, instant messaging and augmented reality capabilities.
• None of the described solutions provides, off-the-shelf, support for multisensory
data that can be combines with the audio visual streams for enriching the media
information with biometric indicators.
In this context, the proposed approach is to leverage the rich media features of
NUBOMEDIA for creating an open source application capable of defeating these
limitations.
6.3.3 NUBOMEDIA outcomes
As specified in Task 6.2 of the, during the last year of the project we shall create a
demonstrator providing professional-grade communications for e-health and
incorporating augmented reality supported multisensory information. This application
shall be released as open source with the aim of democratizing e-health
communications. Eventually, we also plan to present the application into commercial or
engineering conferences.
6.3.4 References
[NILSEN2012] Nilsen, Line Lundvoll. "Collaboration between Professionals: The Use
of Videoconferencing for Delivering E-Health." Future Internet 4.2 (2012): 362-371.
[LUI2008] Liu, Qian, et al. "Securing telehealth applications in a Web-based e-health
portal." Availability, Reliability and Security, 2008. ARES 08. Third International
Conference on. IEEE, 2008.
[VARGHEESE2014] Vargheese, Rajesh, and Prashant Prabhudesai. "A real time
provider identity verification service for a trusted telehealth video
collaboration." Collaborative Computing: Networking, Applications and Worksharing
(CollaborateCom), 2014 International Conference on. IEEE, 2014.
[TAN2002] Tan, Joseph, Winnie Cheng, and William J. Rogers. "From telemedicine to
e-health: Uncovering new frontiers of biomedical research, clinical applications &
public health services delivery." The Journal of Computer Information Systems 42.5
(2002): 7.
[YAN2010] Yan, Hairong, et al. "Wireless sensor network based E-health system??
implementation and experimental results." Consumer Electronics, IEEE Transactions
on 56.4 (2010): 2288-2295.
[SCHREIER2008] Schreier, Günter, et al. "A mobile-phone based teledermatology
system to support self-management of patients suffering from psoriasis." Engineering in
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Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International
Conference of the IEEE. IEEE, 2008.
[FENSLI2005] Fensli, Rune, Einar Gunnarson, and Torstein Gundersen. "A wearable
ECG-recording system for continuous arrhythmia monitoring in a wireless tele-homecare situation." Computer-Based Medical Systems, 2005. Proceedings. 18th IEEE
Symposium on. IEEE, 2005.
[LIU2008b] Liu, Qian, et al. "Securing telehealth applications in a Web-based e-health
portal." Availability, Reliability and Security, 2008. ARES 08. Third International
Conference on. IEEE, 2008.
[STEELE2013] Steele, Robert, and Amanda Lo. "Telehealth and ubiquitous computing
for bandwidth-constrained rural and remote areas." Personal and ubiquitous
computing 17.3 (2013): 533-543.
[CLARKE2008] Clarke, Malcolm, and Chinnaya A. Thiyagarajan. "A systematic
review of technical evaluation in telemedicine systems." Telemedicine and e-health14.2
(2008): 170-183.
[KIAH2014] Kiah, ML Mat, et al. "Design and develop a video conferencing
framework for real-time telemedicine applications using secure group-based
communication architecture." Journal of medical systems 38.10 (2014): 1-11.
[MOORE2008] Moore, Peter Thomas, et al. "Objective video quality measure for
application to tele-echocardiography." Medical & biological engineering &
computing46.8 (2008): 807-813.
[HAMEL2008] Hamel, Mathieu, Rejean Fontaine, and Patrick Boissy. "In-home
telerehabilitation for geriatric patients." Engineering in Medicine and Biology
Magazine, IEEE 27.4 (2008): 29-37.
[HERNANDEZ2001] Hernández, Alfredo, et al. "Real-time ECG transmission via
Internet for nonclinical applications." Information Technology in Biomedicine, IEEE
Transactions on 5.3 (2001): 253-257.
[MOSA2012] Mosa, Abu Saleh M., Illhoi Yoo, and Lincoln Sheets. "A systematic
review of healthcare applications for smartphones." BMC medical informatics and
decision making 12.1 (2012): 67.
[GAMBLE2009] Gamble, Kate Huvane. "Beyond phones." Healthcare Informatics 26
(2009): 23-5.
[BEHRINGER2007] Behringer, Reinhold, et al. Some usability issues of augmented
and mixed reality for e-health applications in the medical domain. Springer Berlin
Heidelberg, 2007.
[GARCIA2014] Garcia, A., et al. "Immersive Augmented Reality for Parkinson Disease
Rehabilitation." Virtual, Augmented Reality and Serious Games for Healthcare 1.
Springer Berlin Heidelberg, 2014. 445-469.
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[NICOLAU2005] Nicolau, S. A., et al. "A complete augmented reality guidance system
for liver punctures: First clinical evaluation." Medical Image Computing and ComputerAssisted Intervention–MICCAI 2005. Springer Berlin Heidelberg, 2005. 539-547.
[ZHU2014] Zhu, Egui, et al. "Augmented reality in healthcare education: an integrative
review." PeerJ 2 (2014): e469.
[NAVRATIL2011] Navratil, Jiri, et al. "Real-time stereoscopic streaming of robotic
surgeries."e-Health Networking Applications and Services (Healthcom), 2011 13th
IEEE International Conference on. IEEE, 2011.
[LIAO2011] Liao, Hongen. "3D Medical Imaging and Augmented Reality for ImageGuided Surgery." Handbook of Augmented Reality. Springer New York, 2011. 589-602.
6.4 Real-time media on games
Real-time gaming has some features that set it apart from the needs of other real-time
communications solutions. And first of all it needs an infrastructure to provide a
satisfactory gaming experience, which starts with dedicated servers, how the game
design and objectives are limited by the infrastructure (bitrate and network performance
among other things), when to use virtualized servers rather than bare-metal servers, how
matchmaking works (the task of pairing players), or how to allocate additional capacity
when demand rises.
The information collected covers two main areas: companies providing real-time media
capabilities (although the term streaming is now prevalent) and those providing
specifically streaming capabilities for gaming entertainment, since they offer some
characteristics that set them apart. We use the term streaming in the gaming context in
the sense of bidirectional or conversational (stateful), since in multiplayer games at least
two players are matched against each other. That is, in most of the official information
checked the term live streaming is understood as real-time bidirectional to sustain
multiplayer interaction, which involves two channels for receiving and sending data
streams.
Most if not all of this companies have a very short history, and have grown mainly by
acquiring other companies of lesser size, be it because of the technology provided or
because the amount of registered users actually consuming the services provided.
Thanks to the increased technical capabilities of network bandwidth in several countries
and devices (game consoles, as well as mobile devices and last generation TV sets), the
emphasis is clearly moving towards real-time interaction.
The information collected is that which is publicly available, mainly from the
companies’ web sites, since we could not find any scholarly article covering this area of
interest, and since changes of all kinds present a higher frequency than in other
industries, with a constant reorganization of active players (ie. Companies acquiring
other companies). The market is expanding and changing at a rapid pace.
Right now this sector is already well populated by many companies trying to make a
profit from entertainment, a large part of which consists of real-time online gaming.
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6.4.1 Description of current SoTA
Real-time multiplayer games services connect multiple players together in a single
game session and transfer data messages between connected players. The typical realtime games show the following characteristics:
•
•
•
•
Network connections are managed to create and maintain a real-time
multiplayer room. This enables network communication between multiple
players in the same game session and lets players send data directly to one
another (matchmaking).
A player uses a user interface (UI) to invite players to join a room, look for
random players for auto-matching, or a combination of both.
Participant and room state information is stored about games services servers
during the lifecycle of the real-time multiplayer game (server stickiness).
Room invitations and updates are sent to players. Notifications appear on all
devices on which the player is logged in (these could be game consoles or gameenabled mobile devices).
The following concepts relate to the typical lifecycle of a real-time multiplayer game:
Room initialization
Internally, the room sets up a peer-to-peer network between participants where
clients can communicate directly with each other.
Room configuration
The number of players is specified (the max number per match allowed in the
room). Depending on different versions of the game, variants of the game can be
used to ensure that only players who are on compatible versions are automatched.
Participants
When players initiate a multiplayer game, they can choose to invite specific
people or have the games services automatically select other participants
randomly via auto-matching. In real-time multiplayer games, auto-matched
participants will appear as anonymous players to each other.
Connected set
As players join or leave the room, the games services actively attempt to create a
network of peer-to-peer connections between all participants. This forms
a connected set of participants in the room, and every player in the connected set
is fully connected to the other players in the set.
In-game networking
The real-time multiplayer engine (most times exposed as an API) is flexible
enough to allow for creating an in-game network for participants over the
underlying peer-to-peer network. One of the participants acts as a 'host' to
establish the authoritative game data first, and then streams this data to the other
connected participants through data streaming.
Invitations
A mobile device user who is sent an invitation will see a notification on devices
where they are logged in. From the invitation object the game can retrieve
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additional details such as the invitation creation timestamp, the invitation ID,
and the player who sent the invitation.
Gameplay
Once the required number of participants for a room have been connected, the
room is considered to be 'filled' and gameplay can begin.
Event notifications
As the status of the room, its participants, or connection status of the participants
change, Google Play games services will send notifications to your game.
Game data interchange
The games services are used to broadcast data to participants in a room, or allow
participants to exchange messages with each other while playing. This is the
same principle used in games with voice activation.
Sending messages
Messages can only be sent to participants who are connected to the room.
Room closure
The game controls are responsible for leaving the room (that is, disconnecting
the room from the servers) when a participant logs out of the game or exits the
real-time portion of the game. Your game should also handle the scenario where
all participants except the local player have left the room. When this happens,
your game should disconnect the local player from the room immediately.
The room is considered 'closed' when all its participants have left the room. At
this point, your game should shut down any game currently in progress, and
make sure to save game data appropriately.
The Game Engine (GE) is the tool for the creation, development and deployment of the
games. Real-time multiplayer online games have requirements beyond those needed for
single-player games. In this case, among other things, the GE is responsible for the
networking, streaming, threading, or memory allocation, in relation with the
infrastructure available.
Real-time games require an infrastructure capable of supporting the specific loads of
bidirectional streaming typical of online multiplayer gaming. We will review a number
of companies offering from basic connectivity to those specialized in game
environments so we could point out how NUBOMEDIA’s platform will go beyond the
current state of technology.
6.4.1.1 General streaming technologies / companies
There are a number of companies that are already offering media streaming capable for
setting up a multiplayer infrastructure. According to public information most are still
using “first-generation” technologies (some still serving content only through Flashenabled players), and NUBOMEDIA’s collection of technologies, most specifically
WebRTC support is only available in two of them. The main reason for commenting on
these companies here is that some have already expressed an interest in moving to
“next-generation” technologies (interactivity through WebRTC), and consequently we
need to observe the current and immediate future evolution.
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The companies with explicit support of WebRTC are:
•
•
Flashphoner, with their product Flashphoner Web Call Server 4.0, which is a
web-streaming or web-SIP solution using WebRTC audio and video streams.
Genband offers real time communications solutions to connect people to each
other and address the demands of users for real time contextual communications.
Among Genband’s products there are WebRTC capabilities in embedded
communications. Genband’s WebRTC solutions extend traditional
communications to any device (including game consoles), on any IP Network, in
any media.
The rest of these companies offer traditional communications:
•
•
•
•
•
•
•
•
Wowza, with the Wowza Streaming Engine, which provides live streaming
from any encoder, delivering live video and audio streams to any player, any
device, over any protocol.
Imgtec, with the Imagination – PowerVR Video Encoder, which includes multistandard SD and HD video encoders. The cores encode video from raw image
data, producing a compliant bit stream in one of several supported formats.
Bitcodin encodes videos from a variety of input locations (e.g. HTTP or FTP
servers, cloud storages, etc.) into single or multiple output qualities
(representations/renditions), which are used for adaptive streaming formats such
as MPEG-DASH or HLS.
Clearleap is providing direct-to-consumer multiscreen streaming across
multiple device and video format families, with full reporting and management
tools. It has been acquired by IBM in December, 2015.
MTG (Modern Times Group) is a holding of companies centered around
content distribution on any device, with a recent focus on eSports, one of the
largest and fastest growing online video entertainment categories, with two
dedicated companies for eSports: ESL, an organizer of online leagues and
tournaments; Dreamhack, runs global eSports leagues, tournaments and
championships (Europe and the US).
Unified Streaming has several products: a CDN, a process supporting both
video on-demand (VoD) and live content; a Broadcast service, supporting many
devices, from tablets, smartphones to set top Boxes, or game consoles (Xbox).
Envivio has several products: Gateway Multiplexer handles live channels from
any compressed broadcast source using variable bit-rate (VBR) or constant bit
rate (CBR); Video Delivery interfaces with multiple content delivery networks
(CDNs). It has been acquired by Ericsson in October, 2015.
Microsoft Azure Media Services covers live and on-demand streaming. From
2014 their offering includes interactive game streaming platforms: Xbox Media
Services (for multiplayer games) and Microsoft Azure for game development.
6.4.1.2 Game streaming platforms / companies
The following companies do offer a platform for interactive games, or are in the process
of providing one during 2016. These are the ones more closely comparable to
NUBOMEDIA (in the sense of real-time communications platform), but as has been
mentioned before, they still use “first-generation” technologies.
•
Twitch offers social video for gamers, with more than 55 million unique users
per month to broadcast, watch and talk about video games, and the video game
ecosystem: game developers, publishers, media outlets, events, user generated
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content, and the entire eSports scene. Twitch was acquired by Amazon on
august, 2014.
Real-time communication is currently limited to a chat channel that gamers use
to communicate among themselves. It is a distributed system written in Go. In
terms of scalability, it delivers hundreds of billions of messages per day to users
who are watching video games via Twitch’s proprietary protocols, as well as
supporting IRC as a communication protocol.
Similar to NUBOMEDIA they offer client applications for iOS, Android, Web,
and in addition game consoles: Xbox ONE, Xbox 360 and PlayStation 4.
•
Valve Corporation is an American video game developer and digital
distribution company. They are behind popular titles such as Dota 2. It also
developed and maintains Source on which most of its games run, and the
software distribution platform Steam, which has led to the Steam Machine, a
line of pre-built gaming computers running SteamOS.
Steam is the world's largest online gaming platform. Steam provides instant
access to more than 1,800 game titles and connects its 35 million active users to
each other to play, share, and modify communities around Valve products. In
terms of scalability, the Steam platform reached a peak of 12.5M+ users on Jan,
4th, 2016. That means users playing, trading and even buying games online
concurrently.
Valve has their own game engine, Source, for character animation, advanced AI,
real-world physics, shader-based rendering, but no explicit capabilities as to the
level of real-time support. The SDK for the engine contains many of the tools
used by Valve to develop assets for their games. It comes with several
command-line programs designed for special functions within the asset pipeline,
as well as a few GUI-based programs designed for handling more complex
functions
•
Amazon is entering new areas of technology, and online real-time gaming is one
of the most recent (from 2014 on). What this means is that virtual game consoles
(Xbox from Microsoft, and others in the future) can be offered through a virtual
emulator in multiscreens (PC, mobile, tablet) at the user convenience, with an
HTML5-capable web browser, and using an original console controller in the
same way as if using the console to play. They currently offer an Xbox cloud
game console (ORBX), Red5 Media Server for video conferences, and multiuser gaming, and NVIDIA GRID GPU for multiplayer DirectX games.
•
Microsoft Media Services for Game Development is a very recent offering
from Azure (Microsoft’s Cloud branch). The concept underlying the
infrastructure is similar to NUBOMEDIA.
It supports online, mobile and social games, with users playing the same game
on multiple platforms and devices, players expecting to receive instant
notifications when the status of their time-based game has changed, and trying to
reach audiences in multiple locations around the world.
With this objective multiplayer game servers are provided as IaaS virtual
machines. Multiplayer game servers are usually based on open source or
licensed frameworks, acting as the source of events for the clients (players)
connected to them and providing information about other players who have
joined the same server. These frameworks require stateful interactions with
clients, as well as access to local storage, provided by the infrastructure.
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A final comment needs to be made about a recent trend that is gaining traction and will
extend to cover multiplayer gaming in the near future, which is eSports broadcasting,
and that NUBOMEDIA does not need to cover. Azubu and Dailymotion Games are
providing gaming, viewing, and interactive experiences. They offer video gamers a way
to live stream their game play so they can review games in front of a live audience and
interact with the public. Although interesting to see how it develops, however, this is an
area that lies outside of NUBOMEDIA’s objectives.
6.4.2 NUBOMEDIA approach beyond SotA
The technologies and business of online multiplayer gaming (or cloud gaming, that is,
using IaaS + PaaS) is mature with an increased number of users-year after year. We find
that the philosophy of Microsoft Azure for Game Development is closer to the
objectives of NUBOMEDIA’s platform, but they are not offering WebRTC official
support yet, and it is not the ambition of NUBOMEDIA to be comparable to Microsoft
Azure (this is an ongoing multi-million dollar investment). NUBOMEDIA itself is just
a platform for real-time media communications, and one of the possible uses could be
for running social interactive games. In NUBOMEDIA we will have capabilities to
enrich the player experience, for instance, recording the activity to be later shared
among the participants of the game is interesting for some types of games (those that
privilege inter-player interaction as the basis of the game).
The technologies in use in the platforms reviewed are still “first-generation”, in some
cases dating back from several years, when current approaches around real-time for
games were not even prototypes. We have not detected widespread use of WebRTC or
other modern real-time technologies in the games building arena. WebRTC is still a
growing field and a lot of improvement will come in future years, but the technology is
viable even in the present scenario. NUBOMEDIA includes more up-to-date
technologies, notably around WebRTC support, as well as some specific APIs that
taken together provide a simpler way for building and running games on a platform that
is not purpose-built for this.
Another factor in multiplayer gaming is the amount of physical infrastructure needed,
with typical side-effects, most notably network latency (or buffering) that hinders the
gaming experience. There are several reports of platforms such as Valve-Steam or
Twitch, from users about latency issues that make real-time gaming a less than pleasant
experience. Twitch even created a specific thread about this topic
(https://www.reddit.com/r/Twitch/comments/3n5m0v/buffering_stream_lag_issues_the_me
gathread/). NUBOMEDIA could provide real-time streaming (with sub-second
latencies, depending on the amount of aggregated elements, such as filters), so this type
of issues will be minimized.
6.4.3 NUBOMEDIA outcomes
ZED’s main outcomes from NUBOMEDIA revolve around testing and validating new
ways of creating multiplayer games. Games with more “layers” than are commonplace
in the current typical games. Successful games nowadays are necessarily multiplayer in
concept and practice. They are more engaging and consequently generate more revenue
than single-user games. ZED will create a social game as a demonstrator of the stack of
technologies composing NUBOMEDIA. The philosophy underlying this demonstrator
is centered on real-time interaction and will be a testbed for experimentation of some
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game concepts. It will be too ambitious to say that this could create the killer app that
WebRTC needs to become the de facto standard.
ZED plans to include NUBOMEDIA’s resulting technologies (either all of them or just
part) into a future generation of games, even exploring new categories where
traditionally ZED had no previous products. For instance, highly interactive game-based
educational material will be made possible in a much easier way using the technologies
of the project. ZED has agreements with a number of game publishers and distributors
and will propose creating these new games using NUBOMEDIA’s outcomes, thereby
simplifying the full game development and deployment lifecycle, where infrastructure
and platform and tooling traditionally take a lot of time and effort. This will result in
savings in time, testing and overall quality.
6.4.4 References
[FLASHPHONER.COM] http://flashphoner.com/
[GENBAND.COM] https://www.genband.com/
[WOWZA.COM] http://www.wowza.com/
[IMGTEC.COM] https://imgtec.com/
[BITCODIN.COM] https://www.bitcodin.com/
[CLEARLEAP.COM] http://clearleap.com/solutions/
[MTG.COM] http://www.mtg.com/our-world/what-we-do/our-digital-products/
[UNIFIEDSREAMING.COM] http://www.unified-streaming.com/
[ENVIVIO.COM] (http://www.envivio.com/
[AZUREMICROSOFT.COM]
services/live-on-demand/
https://azure.microsoft.com/en-us/services/media-
[TWITCH.TV] http://www.twitch.tv/
http://engineering.twitch.tv/
[VALVESOFTWARE.COM] http://www.valvesoftware.com/
Steam http://store.steampowered.com/
Source https://developer.valvesoftware.com/wiki/SDK_Docs
[AMAZON.COM] https://aws.amazon.com/
[MICROSOFT.COM] https://customers.microsoft.com
Azure Xbox https://customers.microsoft.com/Pages/CustomerStory.aspx?recid=13468
MS Game Development https://msdn.microsoft.com/en-us/magazine/dn532200.aspx
[AZUBU.TV] http://www.azubu.tv
[DAILYMOTION.COM] http://games.dailymotion.com/
6.5 Real-time media for social TV
At its very beginning, TV was natively “social”: when a TV screen was too expensive
to be considered “everyone’s technology”, it was quite normal to have shared TV
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screens in public places or even to meet all together with the neighborhood to watch TV
in the evening.
During The 90s, we saw a deep growth in telecommunications. First of all the number
of mobile phones suddenly grew with the advent of GSM, while being this the “brand
new communication way”, the fall in terms of prices and appeal of fixed
communications led to the first “fixed flat rates” offers. In that context we first saw the
some cross-interactions like voting via SMS or Phone call, or having SMS texts shown
on top of the screen, aiming at saying hello to relatives or friends.
In general, "Social TV" is the term used to describe the current integration of social
media interaction with television programming. Social television has sought to
recapture those early days of television, when families gathered in their homes to share
the experience of watching television together. Over the past several years, online social
media communities such as message boards, Twitter, and Facebook have become the
new virtual water cooler for today's tech-savvy television viewers. With the
proliferation of social media applications and Smartphone technology, social interaction
around television programming can now be shared amongst millions of viewers
simultaneously. Twitter and other 2 social media platforms have "become an integral
outlet for TV viewers who look to express themselves while watching broadcasts of
their favorite television programs." This "backchannel" of communication during TV
shows has also led to the resurgence of people's interest in watching live shows.
It has been reported that people watch more live TV to both avoid spoilers and to
communicate with other viewers. This is in contrast to the trend only a few years ago
when people started using DVR machines to watch shows at their own pace. While the
amount of data generated by users in the context of TV is enormous and ripe for data
mining and business analytics, the problem is that the raw data are a noisy stream of
consciousness. Such a limitation is probably one of the reasons why current TV
applications does not actually exploit the possibilities given by Social Data at their top.
As it was mentioned above, many users use Twitter in order to express their opinion
about what they are seeing live on TV, most of the TV shows, particularly talk shows
and talent shows, tend to use twitter in the same way they used SMS during the 90s, by
simply superimposing tweets on top of the screen, this is of course much more simple
than with SMS.
On the opposite side, mobile and tablet applications related to the TV world use social
network mostly for user authentication and as a way to review TV programs, leading to
applications called “Social EPG”. Some TV shows also used specific social network
applications for voting, but it’s not a widely diffused approach.
6.5.1 NUBOMEDIA approach beyond SotA
NUBOMEDIA allows to build brand new services related to the Social World. As it
was mentioned above, the Social world somehow re-discovered the pleasure of sharing
the TV experience, but as the current applications just allow discussion among live
viewers, NUBOMEDIA could even do more. It could allow to mix video-conferencing
with live TV events, allowing final users to keep in contact while watching TV shows
or Live events such as soccer games, or even movies, taking care both to put in
communication the different peers and to keep the synchronization so that viewers see
things happen at the same time.
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Besides this, the possibility to superimpose information in a simple way allows to
simply show or monitor social information directly on a specific video stream, given
that we are able to have a parallel service monitoring social networks and extracting
information in a smart and simple way.
The solution provided by NUBOMEDIA will allow to monitor some statistical and
mood information about a specific video stream: such an application could be useful for
talk show, speeches and other TV shows in general needing to monitor and show the
“social mood” and “social stats” related to a specific broadcasting event.
6.5.2 NUBOMEDIA outcomes
As specified in NUBOMEDIA DoW, during the last year of the project we shall create
a social TV demonstrator that will combine media content with a social monitoring tool
called SDA (Social Data Aggregator).
6.5.3 References
[BENTON2013] Benton, Adrian and Hill, Shawndra. "The Spoiler Effect: Designing
Social TV Content That Promotes Ongoing WOM".
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