A roadmap for traffic engineering in SDN-OpenFlow networks Ian F. Akyildiz

A roadmap for traffic engineering in SDN-OpenFlow networks Ian F. Akyildiz
Computer Networks 71 (2014) 1–30
Contents lists available at ScienceDirect
Computer Networks
journal homepage: www.elsevier.com/locate/comnet
A roadmap for traffic engineering in SDN-OpenFlow networks
Ian F. Akyildiz a, Ahyoung Lee a,⇑, Pu Wang b, Min Luo c, Wu Chou c
a
b
c
Broadband Wireless Networking Lab, School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA
Shannon Lab, Huawei Technologies Co., Ltd., Santa Clara, USA
a r t i c l e
i n f o
Article history:
Available online 19 June 2014
Keywords:
Software-defined networking
OpenFlow
Traffic engineering
Traffic management
Traffic analysis
a b s t r a c t
Software Defined Networking (SDN) is an emerging networking paradigm that separates
the network control plane from the data forwarding plane with the promise to dramatically
improve network resource utilization, simplify network management, reduce operating
cost, and promote innovation and evolution. Although traffic engineering techniques have
been widely exploited in the past and current data networks, such as ATM networks and IP/
MPLS networks, to optimize the performance of communication networks by dynamically
analyzing, predicting, and regulating the behavior of the transmitted data, the unique features of SDN require new traffic engineering techniques that exploit the global network
view, status, and flow patterns/characteristics available for better traffic control and management. This paper surveys the state-of-the-art in traffic engineering for SDNs, and mainly
focuses on four thrusts including flow management, fault tolerance, topology update, and
traffic analysis/characterization. In addition, some existing and representative traffic engineering tools from both industry and academia are explained. Moreover, open research
issues for the realization of SDN traffic engineering solutions are discussed in detail.
Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction
Traffic engineering (TE) is an important mechanism to
optimize the performance of a data network by dynamically analyzing, predicting, and regulating the behavior of
the transmitted data. It has been widely exploited in the
past and current data networks, such as ATM and IP/MPLS
networks. However, these past and current networking
paradigms and their corresponding TE solutions are unfavorable for the next generation networking paradigms
and their network management due to two main reasons.
First, today’s Internet applications require the underlying
network architecture to react in real time and to be
⇑ Corresponding author. Tel.: +1 404 894 6616.
E-mail addresses: [email protected] (I.F. Akyildiz), [email protected]
gatech.edu (A. Lee), [email protected] (P. Wang), [email protected]
com (M. Luo), [email protected] (W. Chou).
http://dx.doi.org/10.1016/j.comnet.2014.06.002
1389-1286/Ó 2014 Elsevier B.V. All rights reserved.
scalable for a large amount of traffic. The architecture
should be able to classify a variety of traffic types from different applications, and to provide a suitable and specific
service for each traffic type in a very short time period
(i.e., order of ms). Secondly, facing the rapid growth in
cloud computing and thus the demand of massive-scale
data centers, a fitting network management should be able
to improve resource utilization for better system performance. Thus, new networking architectures and more
intelligent and efficient TE tools are urgently needed.
The recently emerged Software Defined Networking
(SDN) [1,2] paradigm separates the network control plane
from the data forwarding plane, and provides user applications with a centralized view of the distributed network
states. It includes three layers and interactions between
layers as shown in Fig. 1. The details of the SDN architecture overview are explained as follows: There may be more
than one SDN controller if the network is large-scale or a
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Application Layer
SDN Applications
Traffic
Engineering
Network
Virtualization
Monitoring
QoS
Routing
Business
Applications
North-Bound Open APIs
Control-Plane Layer
SDN Controller
- SDN Controller
South-Bound Open APIs (e.g., OpenFlow )
Data-Plane Layer
- OpenFlow
switches
- Routers
- Other infrastructure
elements
OpenFlow
Switch
OpenFlow
Switch
OpenFlow
Switch
OpenFlow
Switch
Fig. 1. Overview of SDN architecture.
wide-area region network. The control layer globally regulates the network states via network policies in either a
centralized or distributed manner. Due to the unrestricted
access to global network elements and resources, such network policies can be updated timely to react to the current
flow activities. Furthermore, SDN applications exist in the
application layer of the SDN architecture. A set of application programming interfaces (such as North-bound Open
APIs) are supported to communicate between the application layer and the control layer in order to enable common
network services, such as routing, traffic engineering, multicasting, security, access control, bandwidth management,
quality of service (QoS), energy usage, and many other
forms of the network management. In other words, these
interfaces facilitate various business objectives in the network management. On the other hand, the data forwarding
layer can employ programmable OpenFlow switches
through OpenFlow controller, and the switches communicate with the controller via South-bound Open APIs (e.g.,
OpenFlow protocol) [1]. The OpenFlow (OF) protocol provides access to the forwarding plane of a network switch
over the network and enables software programs running
on OF switches to perform packet lookups and forwarding
the packets among the network of switches or routers.
These programmable switches follow the policies of the
SDN/OF controller and forward packets accordingly in
order to determine what path the packets will take
through the network or switches or routers. In short,
through the interactions among these layers, the SDN paradigm allows an unified and global view of complicated
networks, and thus provides a powerful control platform
for the network management over traffic flows. In the literature, most of the work so far is focused on developing the
SDN architecture and with less effort on developing TE
tools for SDN. While current TE mechanisms are
extensively studied in ATM networks, IP-based and
MPLS-based Internet, it is still unclear how these techniques perform under various traffic patterns, and how to
obtain the enormous traffic and resource information efficiently in the entire network when the SDN is deployed. On
the other hand, SDN promises to dramatically simplify the
network management, reduce operating costs, and
promote innovation and evolution in current and future
networks. Such unique features of SDN provide great
incentive for new TE techniques that exploit the global
network view, status, and flow patterns/characteristics
available for better traffic control and management. Therefore we first briefly discuss the classical TE mechanisms
developed for ATM, IP and MPLS networks, and then survey
in detail the state-of-the-art in TE for SDN from both academia and industry perspectives. Then, we examine some
open issues in TE for SDN, and review some recent progresses in extending traditional TE techniques for SDN
networks.
The remainder of the paper is organized as follows.
Early TE issues and mechanisms based on ATM, IP and
MPLS networks are given in Section 2. An overview of
SDN traffic engineering solutions is provided in Section 3.
From Section 4 to Section 7, the major SDN traffic engineering technologies, including flow management, fault
tolerance, topology update, and traffic analysis, are presented, respectively. Existing TE tools for SDN with OF
switches are further introduced in Section 8. The paper is
concluded in Section 9.
2. Lessons learned from the past
Traffic engineering (TE) generally means that the network traffic is measured and analyzed in order to enhance
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
the performance of an operational network at both the
traffic and resource levels [3].
In the late 1980s, ATM (Asynchronous Transfer Mode)
networks were standardized in the telecommunications
industry. At that time, the key objective of TE was to solve
mainly the congestion control problem to meet the diverse
service and performance requirements from multimedia
traffic, due to the increasing demand for multimedia
services (e.g., data, voice, and video).
At the end of the 1990s, IP-QoS routing technology
became more influential over ATM switching, because
the IP-QoS is much simpler and easier to configure in data
networks. As a consequence, IP-QoS hit the market fast and
dramatically increased the popularity of the services
provided over the public Internet. In the late 90s, MPLS
(Multiprotocol Label Switching) has emerged to work
below IP and was an attempt to do simpler traffic engineering in the Internet, especially for the Internet backbones. However, TE for MPLS is still emphasized on the
control and management of the Internet under current
mechanisms and the network elements, because many
control protocols, residing between application layer and
link layer, are built on top of the Internet protocol suite,
therefore failing to provide sufficient and efficient TE
mechanisms for traffic control and management.
In this section, we review the TE concept and mechanisms from the historical perspective as shown in Fig. 2.
Followed by that, we discuss the direction of TE for the
new paradigm architecture of SDN networks.
3
packets and multiplexing technique that supports switching in public and private networks. ATM is capable of
transporting multiple types of services simultaneously on
the same network and all data are placed in cells of the
uniform size. ATM communication is connection-oriented,
which means that a connection must be established before
any cells are sent.
In ATM networks, congestion control is critical to
multimedia services (e.g., voice, data, and video) that are
increasingly demanded and must meet the QoS requirements such as high- throughput, real-time, and lowlatency. The congestion control schemes are categorized
in two methods: reactive control and preventive control.
Reactive control instructs the source nodes to throttle their
traffic flow at the beginning of congestion by giving feedback to them. However, a major problem with reactive
control in high-speed networks is slow with feedback
because the reactive control is invoked for the congestion
after it happens [4]. In preventive control schemes, unlike
reactive control, the source nodes do not wait until congestion actually occurs. Instead, they try to prevent the network from reaching an unacceptable level of congestion.
The most common and effective approach is to control traffic flow at entry points to the network (i.e., at the access
nodes). This approach is especially effective in ATM networks because of its connection-oriented transport where
a decision to admit a new traffic can be made based on
the knowledge of the state of the route which the traffic
would follow. Preventive control for ATM can be performed in three ways: admission control, bandwidth
enforcement, and traffic classification.
2.1. ATM-based traffic engineering
2.1.1. Admission control
In the admission control, the network decides whether
to accept or reject a new connection based on whether
the required QoS requirement of the new request can be
satisfied. When a new connection is requested, the network examines its service requirements (e.g., acceptable
cell transmission delay and loss probability) and traffic
In the late 1980s, Asynchronous Transfer Mode (ATM)
has been developed and selected to enable the full use of
the broadband integrated service digital networks
(B-ISDN). ATM combines circuit switch routing of public
telephone networks, packet switching of private data networks, and the asynchronous multiplexing of packets.
ATM is a form of a cell switching using small fixed-sized
• SDN-OF Traffic Management and Control
(
(e.g.,
C
Centralized
t li d TE and
d control
t l with
ith OF 11.2+
2+ compliant
li t
controllers and capable switches )
TE for SDN-OF Forwarding
g Scheme
• MPLS Traffic Management and Control
(e.g.,
(e g IP/MPLS)
TE Techniques for MPLS Label Routing
• IP Network Traffic Management and Control
(e.g. IPv4, IPv6)
TE Techniques for IP Packet Routing
• ATM Traffic Management and Control
(e.g., ATM/Ethernet)
TE Techniques for ATM Switching
Fig. 2. Traffic engineering from past to future.
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characteristics (e.g., peak rate, average rate, etc.). The network then examines the current load and decides whether
or not to accept the new connection. The cell transmission
delays and the cell loss probabilities are the most commonly applied decision criteria (QoS parameters) in the
admission control. When the transmission delays and cell
loss probabilities are applied in the admission control,
their long-term-time-averaged values have been used [5].
Using a long-term-time-averaged value, however, may
not be sufficient in an ATM network because the network
traffic can change rapidly and dynamically, forcing the network to move from one degree of congestion to another.
The effects of statistical traffic parameters are investigated
on network performance in [6,5,7,8], such as the average
burst length of the traffic sources, the peak rate of each
source, and the number of sources.
2.1.2. Bandwidth enforcement
The traffic volume may be exceeded at the call setup,
which easily overloads the network. In this case, the
admission control alone is not sufficient to handle it, thus
letting the exceeded traffic volume to become an ‘‘elephant
cell’’. After a connection is accepted, the traffic flow of the
connection must be monitored to ensure that the actual
traffic flow conforms to the specified parameters during
the call establishment. Therefore, the bandwidth enforcement mechanism is implemented at the edges of the network. Once an ‘‘elephant cell’’ is detected, the traffic flow
is enforced by discarding and/or buffering the elephant
cells.
The Leaky Bucket method [9] is one of the typical bandwidth enforcement mechanisms used for ATM networks to
enforce the average bandwidth and the burst factor of a
traffic source. One possible implementation of a Leaky
Bucket method is to control the traffic flow by means of
tokens, in which a queuing model is used. When an arriving cell enters a queue, it will be discarded if the queue is
full. To enter the network, a cell must first obtain a token
from the token-pool. If there is no token left, it must wait
in the queue until a new token is generated. In the Leaky
Bucket method, the elephant cells are either discarded or
stored in a buffer even when the network load is light.
Thus, the network resources are wasted. To avoid this
problem, the marking method is proposed in [10]. In this
scheme, elephant cells, rather than being discarded, are
permitted to enter the network with violation tags in their
cell headers. These elephant cells are discarded only when
they arrive at a congested node. If there are no congested
nodes along the routes, the elephant cells are transmitted
without being discarded. Therefore, the total network
throughput can be improved by using the marking method.
2.1.3. Traffic classification
ATM networks must support diverse service and performance requirements. Different traffic streams may have
different delay requirements, even within delay-sensitive
traffic (e.g., voice or video). To support multiple classes of
traffic in ATM networks, priority mechanisms can be used,
rather than uniform control mechanisms, which mean that
different priority levels are given to different classes of
traffic. There are two ways to use priorities: one can use
a priority mechanism as a scheduling method (i.e., queuing
discipline). In this way, different delay requirements can
be satisfied by scheduling delay-sensitive or urgent traffic
first. The second way is to use a priority scheme to control
congestion. In this case, when a network congestion
occurs, different cell loss requirements can be satisfied by
selectively discarding (low priority) cells. Two dynamic
priority schemes, Minimum Laxity Threshold (MLT) and
Queue Length Threshold (QLT) [11], try to reduce the performance degradation for the low priority traffic. In these
dynamic priority schemes, priority level changes with
time. Also priority mechanism can be used as local congestion control schemes to satisfy different cell loss requirements of different classes of traffic. In [12], various traffic
management and congestion control schemes have been
proposed for ATM networks. It seems that there is no single
preferred management method. In general, depending on
the chosen scheme, there are tradeoffs between, the buffer
resources and delay, buffer resources and overhead, or
buffer resources and complexity or cost [13].
2.1.4. Learning from the ATM-based traffic engineering
From the brief review of traffic engineering on ATM networks and learning from the past, we believe that the SDN
controller(s) must include a variety of congestion control
and traffic management schemes, and admission control
policy rules, to support different traffic types from different
applications with different QoS requirements such as realtime applications (e.g., voice or video) or non-real-time
applications (e.g., data), and have to consider the tradeoffs
between load-balance and QoS in the network.
2.2. IP-based traffic engineering
Traffic engineering is an important feature for Internet
providers trying to optimize the network performance
and the traffic delivery. Routing optimization plays a key
role in traffic engineering, i.e., finding efficient routes to
achieve the desired network performance [14]. In [3], Internet traffic engineering is defined as large-scale network
engineering which deals with IP network performance
evaluation and optimization. Typically, the objectives of
traffic engineering include balancing the load distribution
and minimizing the bandwidth consumption in the network, which are similar to ATM-based traffic engineering
as discussed above [14].
In IP networks, the quality of service (QoS) and resilience schemes are also considered as major components
of traffic engineering. Because a variety of new multimedia
applications not only have bandwidth requirements, but
also require other QoS guarantees, such as end-to-end
delay, jitter, packet loss probability, as well as energy efficiency. In addition, the fast resilience schemes are required
to deal with different types of network failures (e.g., network node or link failure) that frequently may happen in
IP networks [15]. In this case, the traffic engineering solutions must consider how to minimize the impact of failures
on network performance and resource utilization. So far,
the most of IP-based traffic engineering solutions in
[16–18], have basic routing schemes that are based on
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the shortest path and load-balancing schemes with the
equally split traffic into equal cost multiple paths.
2.2.1. The shortest path routing
The basic idea of shortest path routing [19] is to set the
link weights of interior gateway protocols (IGPs) according
to the given network topology and traffic demand to control intra-domain traffic in order to meet the traffic engineering objectives. Most large IP networks run interior
gateway protocols (IGPs) such as Open Shortest Path First
(OSPF) or Intermediate System–Intermediate System
(IS–IS) that select paths based on static link weights (such
as cost value assigned at each link). Routers use these protocols to exchange link weights and construct a complete
view of the topology inside the autonomous system (AS).
Then each router computes shortest paths and creates a
table that controls the forwarding of each IP packet to
the next hop in its route [17]. However, shortest path routing does not seem flexible enough to support traffic engineering in a network supporting a different set of
applications. In addition, the changes of static link weight
may affect the routing patterns of the entire set of traffic
flows (such as link failure). Selecting good link weights
depends on having a timely and accurate view of the
current state of the network. Thus, the Simple Network
Management Protocol (SNMP) provides information about
the status of the network elements, either by polling or via
traps. In addition, it is possible to deploy IGP route monitors that track the topology and IGP parameters in the
operational network. The operator also needs an estimate
of the traffic volume between each pair of routers.
2.2.2. The equal-cost multi-path routing
In equal-cost multi-path routing [20], large networks
are typically divided into multiple OSPF/IS–IS areas. In
some cases, the network may have multiple shortest paths
between the same pair of routers. The OSPF and IS–IS protocol specifications do not dictate how routers handle the
presence of multiple shortest paths, because the IGP routing algorithm using static link does not have the flexibility
to divide the traffic among the shortest paths in arbitrary
proportions. Thus, routing based on link weights is not
flexible enough to represent all possible solutions to the
routing problem. Because of the dynamic traffic demands,
the traffic volumes fluctuate over time in practice, and
unexpected failures can result in changes to the network
topology.
In addition, acquiring an exact estimate of the traffic
matrix is difficult. The practical OSPF [21] provides shortest-path-first routing with simple load balancing by
Equal-Cost Multi-Path (ECMP) that enables the traffic to
split evenly amongst equal cost paths. More specifically,
ECMP, based on the Hash function, aims to divide the hash
space into equal-size partitions corresponding to the outbound paths, and forwards packets based on their endpoint
information along the path whose boundaries enveloping
the packets hash value. Although these schemes provide
a good performance when operating with static load balancing, they are unsuitable for the dynamic load balancing
protocols [22], since this static mapping of flows to paths
does not account for either current network utilization or
flow size, which results in collisions that overwhelm router
or switch buffers so that the overall network utilization is
degraded [23].
2.2.3. Learning from the IP-based traffic engineering
Today’s IP data networks are far more complex and difficult to manage due to their data plane, control plane, and
management plane are split and distributed across different network elements [24,25]. To encounter these problems, a 4D architecture as in Fig. 3 is introduced in [24],
which completely separates the routing decision logic from
the protocols that control the interaction between the network elements. The core components of the 4D architecture include the decision plane for a network-wide view
of the network, the data plane for forwarding traffic, the
discovery and dissemination planes for a direct control.
In addition, the Routing Control Platform (RCP) [25] is
introduced, RCP is a logically centralized platform that
separates the IP forwarding plane to provide the scalability
in order to avoid the complexity problems in the internal
Border Gateway Protocol (iBGP) architectures. These ideas
inspire the confidence of SDN researchers and system
developers for a logically separated network with the
SDN controllers and OF switches.
2.3. MPLS-based traffic engineering
Multi-Protocol Label Switching (MPLS) [26,27] was
introduced as an attractive solution to traffic engineering
by addressing the constraints of IP networks. MPLS-based
TE can provide an efficient paradigm for traffic optimization. Most advantages of MPLS traffic engineering rely on
the fact that it can efficiently support the explicit routing
between source and destination, and thus can arbitrarily
split traffic through the network, and highly flexible for
both routing and forwarding optimization purposes [3].
In MPLS-based TE, the routers use the MPLS label-switching paradigm where labels are assigned and distributed
between routers using the Label Distribution Protocol
(LDP). Packets are assigned with labels by the ingress router, and then the packet is forwarded across the network
Network-level objectives
Decision
Networkwide views
Dissemination
Discovery
Data
Fig. 3. 4D architecture [24].
Direct
control
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
using the label switching based on the label, rather than on
the IP header information. At the egress router, the label is
removed and the packet is again forwarded as an IP packet.
After full label information is exchanged in the MPLS network, a Label Switching Path (LSP) is selected between all
routers.
2.3.1. LSP tunnels
One significant feature of MPLS-based TE is the socalled LSP tunnels that are established by a signaling protocol such as the Resource Reservation Protocol (RSVP).
When using the RSVP, the full QoS offerings of integrated
services are made available, because the use of RSVP for
differentiated services (DiffServ) is already defined within
the Internet Engineering Task Force (IETF). The network
resources can be allocated by multiple LSP tunnels that
can be created between two nodes, and the traffic between
the nodes is divided among the tunnels according to some
local policy. However, the scalability and robustness
become issues in MPLS-based TE [28], since the aggregate
traffics are delivered through dedicated LSPs. The total
number of LSPs within an intra-domain such as a ‘‘full
mesh’’ network is O N 2 where N is the number of ingress
and egress routers within a single domain [3], which is
generally considered to be non-scalable with respect to
network protocols [29]. In addition, the path protection
mechanisms (e.g., using backup paths) are necessary in
MPLS-based TE, as otherwise the traffic cannot be automatically delivered through alternative paths if any link
failure occurs in active LSPs [3]. The network management
is an important aspect of traffic engineering over MPLS.
The success of the MPLS approach to traffic engineering
eventually depends on the easiness with which the network can be observed and controlled.
2.3.2. Learning from the MPLS-based traffic engineering
The simplicity of the SDN can alleviate the complexities
of the MPLS control plane with scalability and efficiency at
the same time [30]. The implementation of OF with MPLS
provides much easier and more efficient network management. Thus, the extension of OF switches with MPLS
[31,30], simply match and process the MPLS flows, without
requiring the MPLS per packet processing operations.
3. Overview of SDN traffic engineering
Traffic engineering mechanisms in SDN can be much
more efficiently and intelligently implemented as a centralized TE system compared to the conventional
approaches such as ATM-, IP-, and MPLS-based TEs because
of the major advantages of the SDN architecture. More specifically, SDN provides (1) centralized visibility including
global network information (e.g., network resource limitations or dynamically changing the network status) and global application information (e.g., QoS requirements); (2)
the programmability without having to handle individual
infrastructure elements, i.e., OF switches at the data plane
can be proactively programmed and dynamically reprogrammed by the centralized controller to optimally allocate network resources for network congestion avoidance
and enhanced QoS performance; (3) openness, where data
plane elements (i.e., OF switches), regardless of the vendors, have a unified interface open to the controller for
data plane programming and network status collection;
and (4) multiple flow table pipelines in OF switches can
make flow management more flexible and efficient.
Since the emergence of SDN, it has been applied to a
variety of network environments, (i.e., Enterprise
networks, large-scale data center networks, WiFi/cellular
networks, etc.). TE technology is of critical importance to
the evolution and success of SDNs. As shown in Fig. 4, current traffic engineering mechanisms mainly focus on four
thrusts including flow management, fault tolerance, topology update, and traffic analysis including characterization.
First, according to the basic operation of flow management in SDNs, when a flow arriving at switch does not
match any rules in the flow table, it will be processed as
follows: (1) the first packet of the flow is sent by the
ingress switch to the controller, (2) the forwarding path
for the flow is computed by the controller, (3) the controller sends the appropriate forwarding entries to install in
the flow tables at each switch along the planned path,
and (4) all subsequent packets in the flow or even different
flows with matching (or similar) attributes are forwarded
in the data plane along the path and do not need any control plane action. In this operation, if the aggregated traffic
consists of high number of new flows, a significant overhead can be yielded at both the control plane and data
plane. Moreover, the forwarding rule setup can also take
time, so that the latency can be increased. Therefore, to
solve these problems, traffic engineering mechanisms for
the flow management should be designed to address the
tradeoffs between the latency and load-balance.
Second, to ensure the network reliability, SDN should
have a capability to perform failure recovery transparently
and gracefully, when a failure occurs in the network infrastructure (i.e., controllers, switches and links) [32]. Moreover, a single link or node failure should be recovered
within 50 ms in carrier grade networks [32]. To increase
the networking resiliency of SDN, in OF v1.1+, a fast failover mechanism is introduced for link or node failures, in
which an alternative port and path can be specified,
enabling the switch to change the forwarding path in the
policy based routing without requiring a round trip to
the controller. Although the situation is much improved
with centralized network management, achieving fast failure recovery is still very challenging in SDN, because the
central controller in restoration must calculate new routes
and notify all the affected switches about the recovery
actions immediately. Moreover, the failure recovery needs
to consider the limited memory and flow table resources at
switches.
Third, the topology update mechanism in SDNs focuses
on the planned changes such as the network policy rule
changes, instead of the network element or link failures.
Since the centralized controllers manage all switches in
SDN/OF networks by dynamically configuring the global
network policy rules, a certain level of required consistency of the network policies needs to be guaranteed
across the switches so that each individual packet or flow
should be handled by either the old policy or the new
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Flow Management
7
Fault Tolerance
• Switch Load-Balancing
• Fault Tolerance For Data Plane
• Controller Load-Balancing
• Fault Tolerance For Control Plane
• Multiple Flow Tables
Traffic Engineering
Topology Update
• New Policy Update
- Duplicate Table Entries in Switches
- Time-based Configuration
Traffic Analysis/Characterization
• Monitoring Framework
• Traffic Analysis
• Checking Network Invariants
• Debugging Programming Errors
Fig. 4. The scope of traffic engineering approaches in current SDNs.
policy, but not by the conflicting combinations. Moreover,
during the policy updating time, the affected flows may be
dropped or delayed, which degrades the network QoS performance or leads to wasting network resources. Therefore,
the key challenge in topology update is how SDN controller
can efficiently update the network with required consistency in (near) real time. This would be even more challenging for a large SDN/OF network, where not every
switch can be directly connected to the central controller.
Last but not least, the traffic analysis mechanisms
should include traffic/network monitoring tools, network
invariant checking mechanisms, programming error
debugging software, flow/state data collection, analytics/
mining of patterns/characteristics, etc. In particular, traffic/network monitoring tools are the most important prerequisite for traffic analysis and they are closely related
to all other traffic analysis mechanisms, especially for
detecting the network or link failures, and predicting link
congestion or bottleneck. However, many SDN architectures use the existing flow based network monitoring tools
from traditional IP networks. These methods can lead to
high monitoring overhead and significant switch resource
consumption [33]. Even though OF v1.3 introduced the
flow metering mechanisms, most of the current controllers
(e.g., NOX, POX, Floodlight, etc.) and available switches still
do not provide an adequate support for different flow or
aggregate statistics. In addition, the implementation of a
controller with complex monitoring and analytical functionalities may significantly increase the design complexity [34]. Therefore, new traffic monitoring tools have to
be developed to achieve low complexity, low overhead,
and accurate traffic measurements.
4. Flow management
In SDN, when an OF switch receives a flow that does not
match any rule in the flow entry at a switch, the first packet
of the flow is forwarded to the controller. Accordingly, the
controller decides whether it is required to install a new forwarding rule in the switches, which can lead to the balanced
traffic load in the network. However, this forwarding rule
installation process may take time and yield delay spikes.
Moreover, if a high number of new flows are aggregated at
the end of switches, significant overhead can be yielded at
both the control plane and data plane. Thus, in this section
we survey solutions that aim to avoid this bottleneck in
SDN by considering the tradeoffs between latency and
load-balance. The solutions are described in the following
subsections including switch load-balancing, controller
load-balancing, and multiple flow tables.
4.1. Switch load-balancing
4.1.1. Hash-based ECMP flow forwarding
The hash-based Equal-Cost Multi-Path (ECMP) [20] is a
load-balancing scheme to split flows across available paths
using flow hashing technique. The ECMP-enabled switches
are configured with several possible forwarding paths for a
given subnet. When a packet with multiple candidate
paths arrives, it is forwarded on the one that corresponds
to a hash of selected fields of that packet’s headers modulo
the number of paths [20,23], thus splitting load to each
subnet across multiple paths [23].
A key limitation of ECMP is that two or more large,
long-lived flows can collide on their hash and end up on
the same output port, creating a bottleneck. This static
mapping of flows to paths does not account for either current network utilization or flow size, thus resulting in collisions that overwhelm switch buffers and degrading
overall switch and link utilization [23]. To encounter such
problem, two load-balancing solutions, e.g., Hedera [23]
and Mahout [33], are proposed. Table 1 presents the comparison of Hedera and Mahout schemes.
Hedera [23], is a scalable and dynamic flow scheduling
system to avoid the limitations of ECMP. It has a global
view of routing and traffic demands, collects flow information from switches, computes non-conflicting paths for
flows, and instructs switches to re-route traffic accordingly. In the Hedera architecture, Hedera has a control loop
of two basic steps. (1) When it detects large flows
8
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Table 1
Qualitative overview of Hash-based ECMP flow forwarding schemes.
Hash-based ECMP flow forwarding schemes
Proposed approaches
Elephant flow detection
Process overhead
Bandwidth overhead
Hedera [23]
Mahout [33]
Edge-switch
End-host
Between the controller and the switches
Between the switches and the hosts
High at the switches
High at the hosts
Hedera Controller
Core switches
Aggregation
switches
...
Edge switches
(Detect
elephant flows)
Server
(End-Host)
Fig. 5. Hedera control architecture in a fat-tree network. It detects large flows (such as elephant flows) at the edge switches for flow management.
(‘‘elephant’’ flows) at the edge switches (as depicted in
Fig. 5) – e.g., a new flow event occurs, the switch forwards
it along one of its equal-cost paths, based on a hash on the
flow’s 10-tuple. This path is used until the flow grows and
meets a specified threshold rate (such as 100 Mbps in
Hedera implementation). (2) It estimates the natural
demand of large flows and computes good paths for them.
If the flow grows past the threshold rate, Hedera dynamically calculates an appropriate path for it and installs these
paths on the switches. Hedera uses periodic polling scheduler at the edge switches for collecting flow statistics and
detecting large flows every five seconds to achieve a balance between improving aggregate network utilization
with minimal scheduler overhead on active flows.
Mahout [33] manages flow traffics by requiring timely
detection of significant flows (‘‘elephant’’ flows) that carry
large amount of data. The existing elephant flow detection
methods, such as periodic polling of traffic statistics (e.g.,
NetFlow [35]) from switches or sampling packets (e.g.,
sFlow [36]) from switches, have high monitoring overheads, incurring significant switch resource consumption,
and/or long detection times [33]. Hedera uses periodic
polling for elephant flow detection that pulls the per-flow
statistics from each of its edge-switch. However, the edgeswitch may need to maintain and monitor over 38,400
flow entries if with 32 servers, each server generates 20
new flows per second with a default flow timeout period
of 60 s, and it becomes infeasible in the real switch implementations of OF. To address these problems, the key idea
of Mahout is that it monitors and detects elephant flows at
the end host via a shim layer in the Operating System (as
depicted in Fig. 6), instead of directly monitoring the
switches in the network. In Mahout, when the shim layer
detects that the socket buffer of the flow crosses a chosen
threshold, the shim layer determines that the flow is an
elephant. Then, it marks subsequent packets of that flow
using an in-band signaling mechanism. The switches in
the network are configured to forward these marked packets to the Mahout controller. At this time, the Mahout controller computes the best path for this elephant flow and
installs a flow-specific entry in the rack switch, otherwise
the switches perform the ECMP forwarding action as a
default. This simple approach allows the controller to
detect elephant flows without any switch CPU- and bandwidth-intensive monitoring. This simple approach ensures
that the flows are bottlenecked at the application layer and
not in the network layer. MicroTE [37] is a very similar
approach as Mahout. It is a traffic engineering scheme to
detect significant flows at the end hosts so that when a
large portion of traffic is predicted, then MicroTE routes
them optimally. Otherwise, the flows are managed by the
ECMP scheme with heuristic threshold.
4.1.2. Wildcard rule flow forwarding
OF uses a field of 32-bit (in v1.0-1.1) or 64-bit (in v1.21.3) wildcards that have binary flags in the match. Thus,
using OF flow-match wildcards can reduce the controlplane load [38]. OF is a great concept that simplifies the
network and traffic management in enterprise and data
center environments by enabling flow-level control over
Ethernet switches and providing global network visibility
[39]. However, the central control and global visibility over
all flows require the controller to setup all flows for the
critical path in the network, which is not sufficiently scalable, because using a single central controller for all flow
setups causes both network load bottleneck and latency
[40]. To encounter such problem, the following two
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
9
Mahout Controller
Core switches
Aggregation
switches
Edge switches
Server
(End-Host)
(Detect
elephant
flows)
Mahout Shim Layer for detecting elephant flows in Operating System
at each end-host
Fig. 6. Mahout control architecture in a fat-tree network. It detects large flows (such as elephant flows) at the end of host servers for flow management.
solutions, DevoFlow and DIFANE, have been proposed
[40,39,41].
DevoFlow [40,39] is proposed for reducing the number
of interactions between the controller and switches. This
mechanism implements wildcard OF rules at switches, so
that the switches can make local routing decisions with
matching microflows and the controller maintains the
control over only targeted ‘‘significant flows’’ (such as
‘‘elephant’’ flows) that may be QoS-significant flows. Similarly, an efficient load-balancing architecture is proposed
in [42] which employs a partitioning algorithm for proactively generating wildcard rules, which are installed in
the switches to handle the requests for ‘‘microflows’’ without involving the controller. Then, the switch performs an
‘‘action’’ of rewriting the server IP address and forwarding
the packet to the output port.
DIFANE [41] proposed a distributed flow architecture
for enterprise networks using wildcard rules in the
switches, in such a way that only the switches handle all
data packets in the data plane. For example, if the arrival
traffic flows do not match the cached rules in the ingress
switch, then the ingress switch encapsulates and redirects
the packet to the appropriate authority switch based on
the partition information. The authority switch handles
the packet in the data plane and sends feedback to the
ingress switch to cache the relevant rules locally. Also,
for minimizing overhead at the controller, DIFANE uses
the link-state routing that enables the switches to learn
about the topology changes without involving the controller, and adapts routing quickly. However, this approach
may have a heavy load on the core switches and they do
not provide a load-balancing scheme in their architecture.
4.2. Controller load-balancing
Whenever a flow is initiated in the network, the OF
switch must forward the first packet of the flow to the controller for deciding an appropriate forwarding path. Such a
unique feature of SDN makes the centralized controller
become another performance bottleneck, in addition to
heavy traffic load among switches mentioned in Section 4.1.
In particular, a single and centralized controller cannot
work efficiently, as the whole network grows because of
the increased number of network elements or traffic flows.
Furthermore, while only providing one type of service guarantees, this single controller fails to handle all different
incoming requests. For example, as shown in [43], a current
NOX control platform can only handle 30 K flow initiations
per second with around 10 ms for each flow install time.
This serving ability is insufficient for SDN applications,
especially for the data center scenarios. Therefore, by
designing different deployments of possible multiple controllers, several promising solutions are proposed to avoid
this bottleneck between the controllers and OF switches,
and their results are summarized in Fig. 7. In the following,
we classify these controller deployment solutions into four
categories: (1) logically distributed controller deployment,
(2) physically distributed controller deployment, (3) hierarchical controller deployment, and (4) hybrid controller
deployment. Table 2 presents the comparison of different
schemes of controller load-balancing. Other solutions are
described in the following subsections including the
multi-thread controllers and the generalized controllers
for the controller load-balancing.
4.2.1. Logically distributed controller deployment
HyperFlow [44] is a distributed event-based control
plane for the OF network as a SDN paradigm, which use
OF protocol to configure the switches. Specifically, the
Hyper-Flow can realize a logically centralized network control by using physically distributed control plane in order to
address the scalability while keeping the benefit of network
control centralization. HyperFlow localizes the decision
making to individual controllers for minimizing the control
plane response time to data plane requests, and provides
scalability while keeping the network control logically centralized. Through the synchronization schemes, all the controllers share the same consistent network-wide view and
locally serve requests without actively contacting any
remote node, thus minimizing the flow setup times. More
specifically, the HyperFlow-based network is composed of
OF switches as forwarding elements, NOX controllers
as decision elements, each of which runs an instance
of the HyperFlow controller application, and an event
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Fig. 7. SDN stack.
propagation system for cross-controller communication.
Each switch is connected to the best controller in its proximity. All the controllers have a consistent network-wide
view and run as if they are controlling the whole network.
Towards this, the HyperFlow uses publish/subscribe system to provide persistent storage of published events using
WheelFS [51] for minimizing the cross-site traffics required
to propagate the events (i.e., controllers in a site should get
most of the updates of other sites from nearby controllers
to avoid congesting the cross-region links).
DIFANE [41] has the following two main ideas: (1) The
controller distributes the rules across a subset of the
switches, called authority switches, to scale to large topologies with many rules. (2) The switches handle all packets
in the data plane, i.e., TCAM (the Ternary Content Addressable Memory), at a switch and divert packets through
authority switches as needed to access the appropriate
rules. The rules for diverting packets are naturally
expressed as TCAM entries. The DIFANE architecture consists of a controller that generates the rules and allocates
them to the authority switches. Authority switches can be
a subset of existing switches in the network, or dedicated
switches that have larger memory and processing capability. Upon receiving traffic that does not match the cached
rules, the ingress switch encapsulates and redirects the
packet to the appropriate authority switch based on
the partition information. The authority switch handles
the packet in the data plane and sends feedback to the
ingress switch to cache the relevant rule(s) locally.
Subsequent packets matching the cached rules can be
encapsulated and forwarded directly to the egress switch.
Using link-state routing to compute the path to the authority switch, all data plane functions required in DIFANE can
be expressed with three sets of wildcard rules. (1) Cache
rules are the ingress switches cache rules so that most of
the data traffic hits in the cache and is processed by the
ingress switch. The cache rules are installed by the authority switches in the network. (2) Authority rules are only
stored in authority switches. The controller installs and
updates the authority rules for all the authority switches.
When a packet matches an authority rule, it triggers a control-plane function to install rules in the ingress switch. (3)
Partition rules are installed by the controller in each switch.
The partition rules are a set of coarse-grained rules. With
these partition rules, a packet will always match at least
one rule in the switch and thus always stay in the data
plane. Since all functionalities in DIFANE are expressed
with wildcard rules, DIFANE does not require any dataplane modifications to the switches and only needs minor
software extensions in the control plane of the authority
switches. Thus, DIFANE is a distributed flow management
architecture that distributes rules to authority switches
and handles all data traffic in the fast path.
4.2.2. Physically distributed controller deployment
Onix [45] is a distributed control platform which runs
on a cluster of one or more physical servers. As the control
platform, Onix is responsible for giving the control logic
Table 2
Qualitative overview of difference schemes of controller load-balancing for different types of distributed controllers.
Controller load-balancing
Description
Proposed
approaches
Logically
distributed
controller
deployment
A logically centralized and physically HyperFlow
distributed control plane.
[44]
Hierarchical
controller
deployment
Additional maintenance and subscription management overhead.
Distributed controller’s rules across a subset of the authority switches.
Small overhead between the central controller
and switches and high resource consumption
(i.e., CPU, TCAM space) at switches.
Publish-subscribe method with the NIB database system.
Additional maintenance and subscription management overhead.
BalanceFlow
[46]
One super controller and many normal controllers, where the super controller
is responsible for load balancing among all controllers.
Additional overhead at control plane.
Kandoo [47]
Local controllers execute local applications and each local controller controls
one or some switches.
The root controller controls all local controllers and runs non-local control
applications.
No global network view for the application processes at local controllers.
Centrally controlled cluster of controllers running in equal mode with automatic failover and load balancing while such a controller cluster is targeted
to manage a ‘‘significant-size’’ of a (sub) network.
The controller clusters can be physically distributed to control different (sub)
networks with required synchronization for necessary consistency, while
those distributed controllers can be inter-connected through a service bus
or extended BGP protocol as defined in the software-services defined networking technology.
No full consistency among the distributed controller clusters.
Control platforms distributed on one Onix [45]
or more servers.
Two-level hierarchy for controllers
(local controllers and a logically
centralized root controller).
Disadvantage
Publish-subscribe method with WheelFS file system for cross-controller
communication and global network view sharing.
DIFANE [41]
Physically
distributed
controller
deployment
Summery
Hybrid controller Logically Centralized, but physically SOX/DSOX
distributed clusters of controllers.
[48–50,85]
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Type of
Distribution
controllers
11
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
programmatic access to the network (such as read and
write forwarding table entries). The network control logic
is implemented on top of Onix’s API from their previous
version [52], which determines the desired network
behavior. So the core part of Onix is a useful and general
API for network control that allows for the development
of scalable applications. Using the Onix’s API, a view of
the physical network, control applications can read and
write state to any element in the network, hence keeping
state consistent between the in-network elements and
the control application that runs on multiple Onix servers.
The copy of the network state tracked by Onix is stored in a
data structure named the Network Information Base (NIB),
which is a graph of all network entities within a network
topology. Therefore, Onix provides scalability and reliability by replicating and distributing the NIB data between
multiple running controller instances.
BalanceFlow [46], which employs a similar concept of
distributed controllers from Onix, is a controller load balancing architecture for wide-area OF networks, which can
partition control traffic load among different controller
instances in a more flexible way. BalanceFlow focuses on
controller load balancing that (1) flow-requests will be
dynamically distributed among controllers to achieve quick
response, and (2) the load on an overloaded controller will
be automatically transferred to appropriate low-loaded
controllers to maximize the controller utilization. They presented Controller X action for an alternative flow-requests
spread mechanism, which can be implemented in OF
switches. Using a more flexible way, the controllers can
reactively or proactively install fine-grained or aggregated
flow entries with Controller X action on each switch.
Different flow-requests of each switch can be allocated to
different controllers. All controllers in BalanceFlow maintain their own flow-requests information and publish this
information periodically through a cross-controller communication system to support load balancing. There are
two types of controllers in BalanceFlow network, one super
controller and many normal controllers. The super controller is responsible for balancing the load of all controllers, it
detects controller load imbalance when the average number of flow-requests handled by a controller is larger than
some threshold of the total flow-requests rate in the
network. The threshold is adjustable according to the performance of the super controller, the number of controllers,
and the network environment.
4.2.3. Hierarchical controller deployment
Kandoo [47] creates a two-level hierarchy for controllers: (1) local controllers execute local applications as close
as possible to switches (i.e., applications that process
events locally), and (2) a logically centralized root controller runs non-local control applications (i.e., applications
that require access to the network-wide state). A network
controlled by Kandoo has multiple local controllers and a
logically centralized root controller. These controllers collectively form Kandoo’s distributed control plane. Each
switch is controlled by only one Kandoo controller, and
each Kandoo controller can control multiple switches. If
the root controller needs to install flow-entries on switches
of a local controller, it delegates the requests to the respective local controller.
4.2.4. Multi-thread controllers
To enhance the request processing throughput, multithreaded multi-core SDN controllers have been developed,
which exploit parallelism (i.e., multi-core) architecture of
servers to provide high throughput with scalability at controller. Table 3 gives a qualitative overview of several
multi-thread controllers, and the results of each proposed
approach depends on their testbed conditions. The detailed
description of each controller is given as follow.
Maestro [53] is a multi-threaded SDN controller implemented in Java. Maestro control platform is based on a
server machine with the total 8 cores from two Quad-Core
AMD Opteron-2393 processors with 16 GB of memory.
Actually 7 cores are used for worker threads and one
processor core is used for functionalities (such as Java
programming class management and garbage collection).
In the performance evaluation, the throughput measures
a response time for flow request message sent by its switch
to the controller and it returns to its origin switch. Maestro
achieves a maximum throughput of around 630; 000 rps
(responses per second) with an average delay around
76 ms.
Table 3
Quantitative overview of multi-thread controllers.
Multi-thread controllers
Proposed
approaches
OpenFlow
version
Number of threads used in CPU cores
Maximum throughput
Average
delay
Maestro [53]
v1.0.0
7 (8 cores from 2 Quad-Core AMD
Opteron 2393 processors)
0:63 million rps
76 ms
Beacon [54]
v1.0.0
12 (16 cores from 2 Intel Xeon E5-2670
processors)
12:8 million rps
0:02 ms
NOX-MT [55]
v1.0.0
8 (8 cores from 2 GHz processor)
1:6 million rps
2 ms
SOX [48]
v1.3+
4 (4 cores from 2.4 GHz processor)
0.9 million pps per server; 3.4+ million pps
with 4 servers in the cluster while hitting the I/O limit
N/A
For the completeness of the paper, we include performance numbers publically reported by vendors. It should be cautioned that, as a well known fact, all
these numbers were reported with some specific tests designed by vendors, and no common tests, parameters, and environments are used so far. In
addition, some controllers are very basic in functionality, and therefore would naturally demonstrate better performance.
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Beacon [54] is a multi-threaded SDN controller implemented in Java, to provide a high performance with linear
performance scaling. Beacon control platform is based on a
server machine with total 16 cores from two Intel Xeon
E5–2670 processors with 60:5 GB of memory. The
(IBeaconProvider) interface is used to interact with the Beacon controller and OF switches. Beacon also provides additional Application Programming Interfaces (APIs) that are
built on the core, which consists of a device manger interface
(IDeviceManager) to search for devices (i.e., add, remove, or
update devices); a topology interface (ITopology) to enable
the retrieval of a list of links; an event registration to notify
when links are added or removed; a routing interface (IRoutingEngine) that allows interchangeable routing engine
implementations; a Web interface (IWebManageable) that
allows the developers to implement an interface to add their
own UI elements. In the performance evaluation where all
the controllers with a single-thread in their testbed based
on Cbench (controller benchmarker), it is shown that Beacon has the highest throughput at 1:35 millions rps, and is
followed by NOX with 828; 000 rps, and Maestro with
420; 000 rps (even though Maestro is a multi-threaded controller, however, Maestro with a single-thread model has
less throughput performance compared to NOX which is a
single-thread model controller). In the second test where
the controllers are configured with a different number of
threads, Beacon running from 2 to 12 threads, has the maximum throughput 12:8 million rps, NOX with two to eight
threads, can handle 5:3 million rps, and Maestro with its
maximum of 8 threads achieves 3:5 million rps. The latency
test shows that Beacon has the lowest average response
time around 0:02 ms, while Maestro and NOX are similar
performance each between 0:04 ms and 0:06 ms.
NOX-MT [55] is a multi-threaded SDN controller implemented in C++ based on NOX, which improves a singlethreaded NOX’s throughput and response time. NOX-MT
uses well known optimization techniques including I/O
batching for minimizing the overhead of I/O, and Boost
Asynchronous I/O (ASIO) library for simplifying multithreaded operation. In the experiment, NOX-MT control
platform is based on an 8 core server machine, which handles about 1:8 million rps with an average response time
around 2 ms. Beacon and Maestro have a similar maximum
throughput achieved about 0:5 and 0:4 million rps respectively. In a single-thread in their testbed, the evaluation
result shows that NOX-MT outperforms the others for maximum throughput achieved about 0:4 million rps, and followed by Beacon about 0:1 million rps, and both Maestro
and NOX are almost same performance with lowest
throughput about below 0:1 million rps.
4.2.5. Generalized controllers [48]
Since its first publication in 2009, OF protocols [1] have
been evolving rapidly with the advances in SDN technologies. To enhance the flexibility, reliability, and advanced
networking capabilities, the subsequent standard releases
after OF v1.0, i.e. OF v1.1, v1.2, and v1.3+, gradually introduced many core functionalities such as multi-flow tables
and multi-controllers, in addition to other critical needed
features such as IPv6, MPLS, and flow metering. However,
these desired new capabilities came with a cost, in terms
13
of their renewed complexity and difficulty for efficient system architecture and implementation, for both the controllers and switches. Changes in the latest OF protocols are so
significant and become incompatible with each other. It is
not only because of those newly added features, but also
the message meanings, formats, and parameters are also
revised and modified. Moreover, in a foreseeable future,
SDN/OF based technology will coexist and probably interoperate with existing IP based ones. The reality, however,
is that to design an efficient and powerful controller for
the later versions of SDN/OF protocols has not been that
easy, and controllers currently from both the market place
and open-source community typically support one or certain versions of the OF protocol. This would cause problems
for adaptors of the SDN/OF technology as it can lead to repetitive investments and also possible isolated small networks
fragmented by the non-compatible standard versions, causing substantially increased complexity and management
difficulties. Therefore it would be advantageous to design
an architecture that effectively and efficiently supports the
internetworking of those different protocols and standards,
with one core set of integrated components.
SOX [48], the Smart OF Controller (SOX) is a generalized
SDN controller, developed and introduced in October 2012,
to control SDN/OF based data networking with both OF
v1.0 and v1.2 switches. Apart from being a generalized
SDN/OF controller, SOX designers, for the first time in a
large networking application, adopted and promoted the
use of best software-engineering practice of model driven
architecture (MDA) [48]. Introducing and applying MDA
in SDN is aimed at improving the extensibility, modularity,
usability, consistency, and manageability of SDN. The
extensibility of SOX is demonstrated in its later extensions
and enhancements to support networking with OF v1.0,
v1.2, and v1.3 switches, as well as many new networking
features such as interworking with routers with MPLS,
MPLS/TE, differentiated QoS, and interworking through
BGP with other networking domains [1].
In addition, SOX is multi-threaded and can be deployed
on a clustered environment in equal-equal mode
[48,56,57]. The number of threads/processes or controller
instances in SOX are dynamically adjusted and fluctuated
with the level of network traffic (packet-in rates) to the controller. New instances of controller will be added into the
pool when the average load on existing controller instances
climbs above a pre-set utilization threshold, and live controller instances will be decreased when the average load
on the controller instances drops below a utilization level.
This design offers a balance between the controller response
time/scalability and the computing resource utilization. The
average throughput for a single SOX server is 0.9 million pps
(packet-in per second), while 4 servers in a cluster could
reach 3.4+ million pps while hitting the I/O bottleneck,
and further addition of servers to the cluster would not help.
Each packet-in would generate N+1 responses, while N is the
number of switches used for the given flow. Since they
deploy 3 switches in a triangular topology, compared with
a single switch for most of the other reported testing
(vendors and universities), SOX’s effective rps would be
4 of the packet-in rate: single server 0.9 M 4 = 3.6
million rps, 4 servers 3.4 + M 4 = 13.2 million rps.
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Table 4
Overview of hardware memories for implementing flow tables on OpenFlow switch.
DRAM
SRAM
TCAM
Purpose
Store for all data-plane functions
Search for data-plane logic
Cost
Scalability (Storage)
Low (Costs per Mega-byte)
Very Large (Millions of flows)
Speed (for packet
matching and
distribution)
Throughput (Depending
on speed)
Slow
Store for data-plane
functions
High (Costs per Mega-byte)
Large (Hundreds of thousand
of flows)
High
Slow (A dozen of GbE ports or a couple
of 10GbE ports at line rate)
High (A couple of dozens of
GbE ports at line rate)
Very High (48GbE + 4 10GbE ports or
48 10GbE + 4X40GbE ports at line rate)
Very High (Costs per Mega-bits)
Small (A couple of thousands of flows)
Very high
Fig. 8. Packet flow over multiple flow table pipelines.
4.3. Multiple flow tables
Other consideration is the multiple flow tables for flow
management. Flows are defined by a sequence of packets
for each flow from its origin to destination that share some
common characteristics. At the beginning, the OF specification v1.0-based switches [58] have a single match table
model typically built on TCAM. In this OF concept, a flow
is identified by using its packet header field matching with
a combination of at least 10-tuple including ingress port,
VLAN id, Ethernet, IP, and TCP header fields. These aggregate fields are put into a single flow table in the TCAM of
an OF switch. However, the single table for implementing
flow rules creates a huge ruleset and could result in limited
scale and inability for large scale deployments since TCAM
space is limited and expensive resource as shown in
Table 4. Also it is inefficient to store so many attributes
in a single table with tremendous redundancy and slow
in searching and matching.
To make flow management more flexible and efficient,
OF v1.1[1] introduced the mechanism of multiple flow
tables and actions associated with each flow entry, as
shown in Fig. 8. OF switch can have one or more flow
tables, when a packet arrives at the switch, first the switch
identifies the highest priority matching flow entry, and
then applies instructions or actions based on the flow
fields. The action performs to send the matched data and
action set to next appropriate table in the switch for pipeline processing. Unknown flows that do not match any
flow entries in the multiple flow tables may be forwarded
to the Controller or dropped. Thus, by decomposing the
single flow table (with length of a flow entry with 40 or
so attributes, exceeding 1000 bits with the OF v1.3+) into
multiple more normalized set of tables, such a mechanism
significantly improves TCAM utilization and also speeds up
the matching process.
Additionally, OF v1.3+ supports Meter table for operating QoS requirements at each level of flows from user’s
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Table 5
Qualitative overview of hardware switch features with OpenFlow v1.3-enabled.
Hardware switch features
Vendors
Huawei
[59]
HP [60]
NEC [61]
Product
SN-640
Switch
Series
HP 5900
Switch
Series
PF5200
Switch
IBM [62]
G8264
Switch
Pica8 [63]
PicOSbased
Switch
Series
StrataXGS
Switch
Series
MLXe/
CER/CES
Switch
Series
Broadcom
[64]
Brocade
[65]
Network interface
Maxswitching
capacity
Maxpacket
processing
Latency
OpenFlow v1.3-enabled (Dated by April 17 2014)
Support
MaxOpenFlow
entries
Number of
flow tables
48 10GE + 4 40GE
1.28 Tbps
960 Mpps
300 ms 400 ms
OpenFlow v1.2/ v1.3
(Q2 2013)
630 K
48 1GE/
10GE + 4 40GE, etc.
1.28 Tbps
952 Mpps
1:5 ls
OpenFlow v1.0/v1.3
(Q2 2013)
N/A
More than
3 (9 stage
pipelines)
N/A
48 0.01GE/0.1GE/
1GE + 4 1GE-OF/
10GE
48 1GE/
10GE + 4 40GE
0.176 Tbps
131 Mpps
N/A
OpenFlow v1.0/
v1.3.1 (Q3 2013)
160 k
N/A
1.28 Tbps
960 Mpps
880 ms
N/A
N/A
1GE/10GE/40 GE
1.28 Tbps
960 Mpps
1:0 ls
OpenFlow v1.3.1
(with IBM
Networking OS 7.8)
(Q4 2014)
OpenFlow v1.3 (Q1/
Q2 2014)
N/A
N/A
32X40GE/(100+)
X1GE/10GE, etc.
1.28 Tbps
N/A
N/A
N/A
8 (6 stage
pipelines)
10GE/40GE/100GE
25.6 Tbps
19 Bpps
N/A
128 K
N/A
OpenFlow v1.3.1 (Q1
2014/Ready to
support)
OpenFlow v1.3
(Ready to support Jun
2014)
Another important comparison is whether the switches truly support multi-flow table pipelines optimized for application’s scenarios. However, some
switches have only one or few table(s).
demanded or different application’s traffic. Some switch
vendors have been started to develop OF v1.3-enabled
switches as for traffic engineer to effectively handling
flows while increasing performance, scalability, and flexibility in SDN paradigm. Table 5 presents a qualitative overview of hardware switch features with OF v1.3-enabled.
4.4. Open research issues
So far we discussed many flow management mechanisms developed for SDN networks. The majority of the
proposed solutions focuses on load-balancing problem in
both data and control planes. There are still many open
research problems within the flow management in SDNs:
Dynamic load-balancing scheme for the data-plane layer: In
order to achieve load-balancing with low-latency
network performance and avoiding network bottleneck
in SDNs, we introduced two major flow forwarding
approaches in Sections 4.1.1 and 4.1.2 based on Hashbased ECMP and Wildcard rule. The common objectives
of both approaches are how to efficiently detect
‘‘elephant’’ flows, i.e., extremely large flows, by using
the conventional ECMP scheme and the Wildcard rule.
These can be implemented by the OF specification [1].
However, these load-balancing schemes are either static
by setting heuristic fixed threshold at edge devices (e.g.,
Hash-based ECMP flow forwarding scheme) or of little
adaptability to flow dynamics (e.g., Wildcard flow
matching rule scheme). The effectiveness of loadbalancing solutions are directly related to traffic
characteristics and link capacities in a given network.
For example, data center traffic can traverse through
the edge, aggregation, and core links with different link
capacities. It is identified that the data center traffic on
edge and aggregation links is more bursty than that on
core links. Such difference in traffic burstiness leads to
high packet loss rates of the underutilized edge and
aggregation links, and low packet loss rates of highly utilized core links [66,67]. Therefore, the traffic engineering
in SDN demands a dynamic load-balancing mechanism
that is dynamically adaptive to time-varying network
states and fine-grained traffic characteristics such as
traffic burstiness and inter-arrival times.
Dynamic load-balancing scheme for the control-plane
layer: There are two major deployments introduced in
Sections 4.2.1, 4.2.2, 4.2.2 and 4.2.4 for distributed controllers to avoid a significant bottleneck at the single
centralized controller in the large-scale SDN network.
One is the hardware system-based mechanism that the
controllers are distributed at different locations such
as the physically separated servers, or controller’s operations are split down the different-level hierarchy, and
also including the hybrid controller approach. The other
one is the operating system-based mechanism such as
the multi-thread controllers. However, the load balancing schemes for control plane are largely unexploited. In
particular, the control plane load balancing solutions
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
need to solve a set of fundamental problems, which aim
to find the optimal number, locations, workload
distribution, control message forwarding paths of SDN
controllers in such a way that the optimal balance
between the control message delay performance and
control overhead/cost can be achieved subject to control/data message traffic statistics and network topology
diversity. There exist very few papers that address the
controller load balancing problem in the literature. In
[68], the controller placement problem is investigated,
where the distance between a controller and switches
is adopted as the performance metric and several wellknown network topologies are evaluated through simulations to find the optimal controller location. In [46],
the controller placement problem is further investigated
by proposing two heuristic algorithms to determine the
proper number and locations of controllers with an
objective to minimize the flow setup time and communication overhead. The controller workload distribution
problem is studied in [46], where a heuristic algorithm is
proposed to adjust the workload of each controller
dynamically according to average flow-requests in all
switches and switch-to-controller latency. Nevertheless,
these efforts only look for quantitative or even heuristic
results than qualitative analysis. In addition, there is
lack of a thorough study to bring traffic statistics into
control message load balancing.
Adaptive multi-flow table schemes: The number of flows
managed by a switch is limited by the size of its flow
tables, because the scalability of using multiple
flow tables is limited due to their very small size and
high cost of TCAM space as shown in Table 4. In general,
TCAM-based tables are limited to a few thousand
entries. However, practically a single switch in data
center can handle more than 100 million packet flows
per second. Thus, the flexible and adaptive flow table
methods should be developed so that the new flows
exceeded from the limited space of TCAMs will be
replaced to the large and lower cost SRAM or DRAM
spaces. These methods should be associated with a traffic scheduling method for different QoS flows. Although
some methods, such as the RMT (Reconfigurable Match
Tables) model [69] and the FlowAdapter [70], have been
proposed to address some challenging issues caused by
the resource constraint of TCAM-based tables, there are
some open issues with the implementation of a multiflow table pipeline in current switching hardware. For
example, how can multiple flow tables be converted
efficiently to the different hardware capabilities? How
can the optimal number of multiple flow tables in the
pipeline be determined since it is dependent on switching hardware and also application scenarios?
5. Fault tolerance
To ensure network reliability, SDN should have a capability to perform failure recovery transparently and gracefully, when failures occur in the network infrastructure
(i.e., controllers, switches and links) [32]. More specifically,
as required by carrier grade networks, TE mechanisms
must provide fast failure recovery so that carrier grade
networks can detect and recover from incidents without
significantly impacting users. In addition, even though a
switch could identify the failed link, it has neither the
intelligence nor the global knowledge to establish a new
route. It must depend on the updates from the controller
to establish an alternate route. Until a controller identifies
a failed link and updates flow table entries in all the relevant switches, the packet that are supposed to travel on
the failed link will be dropped. In the case of switch failure,
even though the controller can sense the failure of a switch
and the use of the fast failover mode could help switching
the traffic to the protection path, but when the failed node
comes back to work, it will still be the responsibilities of
the controller to re-establish the network topology and
the optimal routes for the on-going traffic.
Despite its great importance, achieving fast failure
recovery, e.g., within 50 ms, is a quite challenging task
for SDN, because the central controller in restoration must
compute new routes and notify all the affected switches
about a recovery action immediately. In this section, we
investigate current research efforts on realizing fast failure
recovery in SDN networks.
5.1. Fault tolerance for data plane
5.1.1. Data plane failure recovery mechanisms
There are two types of failure recovery mechanisms for
the network element and link failures: restoration and
protection [71,75,76].
Restoration: the recovery paths can be either
pre-planned or dynamically allocated, but resources
are not reserved until failure occurs. When a failure
occurs additional signaling is needed to establish the
restoration path.
Protection: the paths are pre-planned and reserved
before a failure occurs. When a failure occurs, no additional signaling is needed to establish the protection
path.
Apparently, restoration is a reactive strategy while
protection is a proactive strategy. The restoration and
protection solutions for SDN/OF networks often work as
follows. The qualitative overview of those solutions are
summarized in Table 6.
Data plane restoration [71,72]: After the controller gets
notification of a link failure, a list is made of all affected
paths. For all these affected paths, a restoration path is
calculated using a shortest path algorithm on the
remaining topology. For affected switches which are
on both the working and the restoration path, the flow
entry is modified. For the other switches, there are 2
possibilities. If the switches are only on the failed path,
the entries are deleted. If they are only on the restoration path, the new entries are added.
Data plane protection [73,74]: In this operation, the protection path is pre-computed and it is installed together
with the working path into the flow entries at switches,
such that each switch has two forwarding information one for the protection path and the other for the
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Table 6
Qualitative overview of different schemes of fault tolerance for data plane.
Fault tolerance for data plane
Proposed approaches
Failure recovery schemes
Maximum restoration
time
Maximum protection
time
Fast failure recovery scheme [71,72]
Carrier-grade recovery scheme [73]
Data plane restoration
Data plane restoration and
protection
Data plane protection
ð80—130 msÞ > 50 ms
60 ms > 50 ms
N/A
ð42—48 msÞ < 50 ms
N/A
64 ms > 50 ms
OpenFlow-based segment protection (OSP) scheme
[74]
original working path. Once the failure is detected, e.g.,
via Bidirectional Forwarding Detection (BFD) [77], in
the working path, the switch will use the protection
path for flow forwarding.
Performance comparison of restoration and protection
solutions: Compared with restoration solution that requires
the deletion, modification, and addition operations between
the controller and the switches during failures, the protection scheme can enable faster recovery without involving
controller when failures are detected. Moreover, since protection mechanism is implemented at the switches by preinstalling the protection path, this would slightly increase
the operations at flow setup time because extra protection
information needs to be sent to the switches. However, in
path protection, the bandwidth and latency requirements
during failures can be significantly reduced because no
interactions are required between switches and controller.
For example, according to the experiments on a SDN network testbed with 14 switches [73], using the protection
scheme, the maximum restoration time after failure detection is about 60 ms and all the flows are restored between
42 and 48 ms. This meets the 50 ms failure recovery time
required by carrier-grade network. However, using restoration schemes, the failure recovery time can be in the range of
200—300 ms [71,72]. Therefore, for large-scale SDN systems, path protection solutions are more favorable in terms
of achieving fast failure recovery.
5.1.2. Additional factors impacting fast failure recovery
Besides the centralized controller, the delay in failure
recovery can be also caused by OF protocol. Specifically,
according to OF specification, even if new flow entries are
updated at the affected switch, the switch does not delete
the entries using the failed link until the timeout of one of
their associated timers, i.e., hard timer and soft timer,
which is normally in the range of several seconds. This
means that path failures are not actually recovered until
one of the aforementioned timers expires. To encounter
such problems, in [71,72], the protection or backup paths
are pre-computed and installed using the GroupTable functionality of OF specification v1.1 as the ‘‘fast failover’’ mode.
Once the failure in the working path is identified, the action
bucket associated with this path in the GroupTable is made
unavailable immediately by changing the value of its alive
status. As a consequence, the packet arriving at the switch
will be treated according to the next available bucket associated with the protection path. Instead of using GroupTable, OF-based segment protection (OSP) scheme [74]
employs flow entry priority and auto-reject mechanism to
realize fast switch-over between working path and protection path. More specifically, by assigning high priority to
working path entries and low priority to protection path
entries, it is guaranteed that all flows are forwarded via
the working path if no failures are detected. Upon failures
detected, the auto-reject mechanism allows all affected
flow entries using the failed links to be deleted immediately
without waiting for the soft or hard timeout. By this way,
the affected flows can be timely restored and deviated by
the switches so that they can research their destinations
using the protection paths.
To achieve fast failure recovery, it is also demanding to
inform the switches affected by the link failures as soon as
possible. Moreover, this can effectively avoid waste of
bandwidth by stopping the relevant switches from sending
messages towards the direction of the failed links. Towards
this, an algorithm is developed in [78], which allows
switches to exchange simple link failure messages (LFMs)
in such a way that the relevant switches can be aware of
a link failure in a much shorter time than what it takes
for controller to identify a link failure and send out the
topology update. The advantage of this algorithm is that
it does not involve controller, while no control message
needed to be flooded in the entire network. However, the
performance of this algorithm depends on the number of
switches, i.e., if a network has a lot of switches that send
flows towards the failed link, it will take longer to send
LFM to all of them, and also depend on the total number
of flow table entries in a switch, i.e., the larger the number
of flow table entries, the longer it takes to search for the
flows that go towards the failed link.
5.2. Fault tolerance for control plane
Because SDN is a logical centralized architecture, which
relies on the controller to update policies and take actions
when new flows are introduced in the network, reliability
of the control plane is of critical importance. Without
resolving a single point failure in the control plane, the
entire network may be negatively recovered. The most fundamental mechanism to recover control plane failures in
the centralized network is the ‘‘primary-backup replication’’ approach, where backup controllers will resume the
network control in the case of primary controllers failures
[79]. Two problems have to be addressed to support the
replication schemes in SDN.
Coordination protocols between primary and backup
controllers: The OF protocol provides the possibility to
configure one or more backup controllers, but OF does
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
not provide any coordination mechanism between the
primary controller and the backups. Thus, the coordination protocols are needed, which are capable of
performing the coordination between controllers to
keep the backup consistent with the primary, which
will turn the network to a safe state with minimal
overhead imposed on hosts and switches [80].
Backup controller deployment: The problem of placing
controllers in SDNs aims to maximize the reliability of
control networks. Specifically, (1) the impact of the controller number on reliability needs to be determined,
and (2) the tradeoffs between reliability and latencies
should be considered [81,68].
5.2.1. Primary and backup controller coordination
A new component called CPRecovery, which runs independently on top of the network OS, is developed in [80] to
support the primary-backup mechanism. By CPRecovery,
the replication process between the switch component
running on the primary controller and the secondary controller works as follows: the switch sends an inactivity
probe with an amount of a waiting time setup via the connection with the controller. If the controller does not send
a reply within the waiting time, the switch assumes that
the controller is down. In the recovery phase, the CPRecovery component acts during a failure state of the primary
controller; the switch searches for the next network OS
(the secondary controller acting as a backup) in its list
and starts a connection to it. If the secondary controller
receives a connection request from the switch, it generates
a data path join event and changes its internal state to
primary controller (become a primary controller). The
new current primary keeps trying to send the state update
messages to the former primary controller (become a
secondary controller). For the experimental results, the
response time is evaluated with different replication
degrees in [80]. (The response time includes that the controller takes to process a request from the switch, sends a
state update message to the backup controllers, receives
a confirmation and sends a confirmation to the switch.)
Four controllers are used in their experiments, one for
the primary, three for the backup controllers (the replication degree = 3). The average response time without any
secondary controller is around 8 ms, and the average
response time with the replication degree = 1 (such as
one secondary controller added) is around 14 ms. The
75% increase of the average response time is because it
takes time to send and receive a confirmation from the secondary controller. Thus, the increased response time
depends on the increased replication degree.
5.2.2. Backup controller deployment
In [68], the impact of the number of controllers on
latency and reliability is analyzed, based on the average
and worst-case propagation latencies on real topologies
using the Internet2 OS3E [82]. From the analysis results
of the Internet2 OS3E, it is shown that the average latency
of one controller may be reduced to half by three controllers, while the same reduction for worst-case latency
requires 4 controllers. Hence, the latency is deceased by
1=k controller for the single-controller latency. A ð3 þ 1Þ
controller setup is suggested in [68] for three load-balancing controllers and one backup controller for fault tolerance in the SDN architecture. Moreover, it is shown that
one controller with a 10 ms latency (as a response time
between the controller and the switches when a new
packet flow is requested) is enough to meet a specified
latency bound given the network size is between 8 and
200 nodes.
The problem of placing controllers is further addressed
in [81] by investigating the impact of controller number on
the tradeoffs between reliability and latency. The simulations are based on real topologies using the Internet2
OS3E [82] (a testbed of the multiple controllers for SDN)
and Rocketfuel [83] (to analyze the actual values at the
router-level maps of the ISP network). The failure probabilities of each switch and each link are set with the 0.01 and
0.02, respectively. According to the experiments, the best
controller number is between ½0:035n; 0:117n (where n is
the total number of network nodes) as the minimum
number of controllers is 3 and the maximum number of
controllers is assumed to be 11. To determine the tradeoffs
between the reliability and latency, it is suggested that the
best controller placement is using one controller that
yields the optimal reliability metric, while optimizing the
average latency. However, when 3–4 controllers are
placed, optimizing average and worst-case latency
decrease the reliability. On the other hand, optimizing reliability increases the average and worst-case latency.
5.2.3. Distributed controller clusters in equal mode with a
logical central view
Based on the possible deployment of SDN/OF controllers in equal mode as introduced in OF v1.2 [1], SOX [48]
takes the approach of a centralized controller clusters
while many controllers could be concurrently run in equal
mode and the cluster shares a common Network Information Base (NIB). Such an architecture enabled automatic
fail-over and load balancing, while the number of controller instanced would increase or decrease dynamically by
adapting to the changing traffic demands. It is first demonstrated in the ONF PlugFest in October 2012 [48] and
showcased in the first SDN World Congress [84].
Later a distributed SOX (DSOX) [85] is designed in
which each centralized cluster is aimed to serve a large
metropolitan area, or a particular autonomous system
(AS). It utilizes a centralized NIB with the required
information for globally optimized routing and resource
scheduling, which has a globally centralized view and control over all its distributed domains. It should be noted that
the designers of DSOX intentionally tried to limit the
amount of data synchronization for consistency, and the
newly updated domain network states or key traffic statistics would either be updated periodically or triggered by
some special events.
5.3. Open research issues
Although some fault tolerance mechanisms are proposed at both data and control planes, there are still many
open research problems to achieve a high reliability in SDN
networks:
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Fast and cost-efficient failure recovery for data plane:
From the research contributions discussed above, it is
clear that the protection mechanism is the most appropriate approach for a high reliability performance with
low-overhead communications between the controller
and the switches in SDNs. However, this mechanism
consumes high memory resources because the protection forwarding table may be installed on TCAM at each
switch. Moreover, if the network policy is changed, then
the pre-installed protection forwarding table should be
updated too by following the new network policy,
which can produce additional communication and
operation overhead between the controller and the
switches in the SDN network. Therefore, fast failure
recovery mechanisms should be implemented in such
a way that the fast failure recovery can be achieved
with low communication overhead, no/less interference
to SDN controller, and with the minimum intelligence
available at the switches.
Traffic-adaptive primary-backup replication for control
plane: The centralized control plane has the critical reliability issue, such as a single point failure. To solve this
problem, the primary-backup replication approach is
commonly used in the centralized network. However,
there are several open problems regarding how to
define an optimal number of controllers and the best
locations of the controllers for the primary control and
the backup control(s) with an optimal tradeoff between
reliability and latencies for time-varying traffic
patterns, such as traffic volume trends in the entire network. These challenging issues should be accounted
into the implementation of the fault tolerance mechanism in order to achieve a high reliability and an
optimal performance of SDN controller(s).
6. Topology update
In this section, we focus on the planned changes (such
as the network policy rules changes), instead of unplanned
events (such as the network element/link failures) [86].
The general update operations are implemented as
follows: each packet or flow is identified when updating
the network from old policy to the new policy over multiple switches, and then each individual packet or flow is
guaranteed to be handled by either the old policy or the
new policy, but not by the combination of the two [86].
There are two types of consistency.
Per-packet consistency: means that each packet flowing
through the network will be processed according to a
single network configuration.
Per-flow consistency: means that all packets in the same
flow will be handled by the same version of the policy.
Hence, the per-flow abstraction preserves all path properties. These properties are expressed by the sets of
packets belonging to the same flow that go through
the network.
The key challenges are how SDN controller efficiently
updates the network with consistency and also in real
time.
19
6.1. Duplicate table entries in switches
To implement a consistent update for per-packet, a simple generic idea is proposed in [86,87], where the controller installs new configuration rules on all of the switches in
a header field with the new version number; the ingress
switches mark their new policy with the version number
to incoming packets; meanwhile other switches can process packet with either the old or new policy, depending
on the version number on the packet, but any individual
packet is handled by only one policy; once all packets following the old policy have left the network, the controller
deletes the old configuration rules from all switches, and
then the update configuration is done. The efficiency of this
algorithm depends on the explicit information on how long
the switches need to hold the old rules because the limited
memory, particularly the Ternary Content Addressable
Memory (TCAM) at switches is not sufficient to hold a
large-size forwarding tables configured by both old and
new rules. Per-flow consistency guarantees that all packets
in the same flow will be handled by the same version of the
policy as long as its rule imposed on the flow does not time
out. In particular, when the controller installs the new configuration, it sets a timeout for the old configuration rule.
During this period, the incoming flows are handled by
the old version until the rule expires. However, this algorithm only considers the scenario where multiple flows
are processed using the same rule while leaving problem
of handle flows with different rules open.
The key problem of above duplicated table entries
scheme is that it requires holding both old and new sets
of rules on the network switches. In the worst case, the
switches holding the both policy rules can have a 100%
overhead in terms of rule space resource consumption on
the switches. To solve this problem, a more efficient
update algorithm is introduced in [88] by adding a transfer
function f s at each switch s to perform policy rule replacement from old to new with high flow initiation data rate
between the controller and the switches. A similar work
can be found in [89], which introduces a generic update
algorithm for implementing consistent update that
considers a tradeoff between update time and rule-space
overheads as in [86,87].
6.2. Time-based configuration
Time-based configuration method is introduced in
[90,91] to allow coordinated SDN network updates in
multiple devices, such that the controller can invoke a coordinated configuration change by sending update messages
to multiple switches within either the same scheduled
execution time or the different scheduled time, based on
a time-based sequence of different update times. This
approach was designed to simplify complex update procedures and to minimize transient effects caused by configuration changes. The implementation is very simple. For
example, the controller sends a new updated policy with
different time-based updates to each switch in such a
way that switch 1 is scheduled to update its configuration
with the new policy at time t, switch 2 at tþ, and so on.
The big problem of this solution is that in OF networks,
20
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
Table 7
Qualitative overview of different schemes of monitoring framework.
Monitoring tools
Proposed
approaches
Description
Traffic engineering technology
PayLess [34]
Query-based
monitoring
Adaptive polling based on a variable frequency flow statistics collection algorithm.
High accuracy and high overhead
within a short minimum polling
interval.
Low accuracy and low overhead
within a large minimum polling
interval.
OpenTM [94]
Query-based
monitoring
Periodically polling the switch on each active flow for collecting
flow-level statistics.
High accuracy and high overhead.
FlowSense
[95]
Passive pushbased
monitoring
Using the PacketIn and FlowRemoved messages in OpenFlow networks to estimate per flow link utilization for reducing monitoring
overhead.
High accuracy and low overhead
compared with the Polling method.
OpenSketch
[96]
Query-based
monitoring
Using the wildcard rules at switches for only monitoring a large
aggregate of flows instead of all flows for reducing monitoring
overhead.
Using a hierarchical heavy hitter algorithm for achieving high
accuracy.
Low memory consumption with high
accuracy.
MicroTE [37]
Push-based
monitoring
Implemented on a server machine separated from the controller
machine.
Advantages: (a) allows MicroTE to proactively respond when traffic
demands change significantly, (b) reduces the processing overhead
at the controller for collecting flow statistics, and (c) allows MicroTE
to scale to a large network.
Low consumed network utilization.
OpenSample
[97]
Push-based
monitoring
Using the packet sampling tool sFlow [36] and TCP sequence numbers for achieving low latency.
Enabling traffic engineering to fast detect elephant flows and estimate link utilization of every switch port.
Low latency measurement with high
accuracy for both network load and
elephant flows.
the controller must wait for an acknowledgment from one
switch for completing the update before sending the
update new policy to other switch until the network is
completely updated.
A real-time network policy checking approach called
Net-Plumber, is proposed in [92], which is based on Header
Space Analysis (HSA) [93], and is able to configure the forwarding table with significantly fast update time. The
NetPlumber Agent sits between the control plane and the
switches, and it uses the HSA algorithm that can be used
to check a rule update against a single policy within
50—500 ls. Instead of updating all the switches simultaneously, it incrementally updates only the portions of the
switches affected by the changing rules in the network
by using the plumbing graph that caches all possible paths
of flows over the network to quickly update the reachable
switches of a path for the flow, which is filtered by the OF
rule (e.g., match, action). By this approach, it can update
the network policy changes in real-time.
6.3. Open research issues
A consistency of topology update schemes may be considered in two different network scenarios.
A single controller in the SDN network: How can the SDN
controller efficiently update the network information
with consistency in real-time without packet losses?
Multiple controllers in the multi-domain SDN networks: If
there are multiple SDN controllers in the large-scale or
wide-area region network, then how can they
Analysis
consistently update the shared network information in
the entire network with the tradeoff between the low
inter-synchronization overhead and the real-time
update?
7. SDN traffic analysis
In this section we discuss current network monitoring
tools for network management, network verification and
debugging in SDN architectures. The qualitative overview
of the different monitoring solutions is summarized in
Table 7.
7.1. Monitoring framework
Monitoring is crucial for network management. The
management applications require accurate and timely statistics on network resources at different aggregation levels
(such as flow, packet and port) [34]. The flow-based programmable networks, such as SDNs, must continuously
monitor performance metrics, such as link utilization, in
order to quickly adapt forwarding rules in response to
changes in workload. However, existing monitoring solutions either require special instrumentation of the network
or impose significant measurement overhead [95]. Many
SDN architectures use the existing flow based network
monitoring tools from traditional IP networks. For instance,
the most prevalent one is NetFlow [35] from Cisco, which
uses probe methods that are installed at switches as special
modules to collect either complete or sampled traffic
statistics, and send them to a central collector [82]. Another
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
flow sampling method is sFlow [36] from InMon, which
uses time-based sampling for capturing traffic information.
Another proprietary flow sampling method is JFlow [98],
developed by the Juniper Networks. JFlow is quite similar
to NetFlow. However, these approaches may be not
efficient solutions to be applied in SDN systems, such as
large-scale data center networks, because of the significantly increased overhead incurred by statistics collection
from the whole network at the central controller. Therefore,
the following solutions are seeking more efficient monitoring mechanisms in order to achieve both high accuracy and
low overhead.
PayLess [34] is a query-based monitoring framework
for SDN to provide a flexible RESTful API for flow statistics
collection at different aggregation levels (such as flow,
packet and port), where it performs highly accurate information gathering in real-time without incurring significant
network overhead. To achieve this goal, instead of letting
controller continuously polling switches, an adaptive
scheduling algorithm for polling is proposed to achieve
the same level of accuracy as continuous polling with
much less communication overhead. Moreover, PayLess
provides a high-level RESTful API, which can be accessed
by any programming language. Therefore, it is very easy
for different network applications to develop their own
monitoring applications, and access the collected data
from the PayLess data stored at different aggregation levels. The evaluation results show that PayLess has a very
low overhead of only sending 6.6 monitoring messages
per second on the average, compared with the controller’s
periodic polling, which has an overhead with 13:5 monitoring messages per second on average. The measurement
of the trade-offs between accuracy and the monitoring
overhead within the given minimum polling interval (T min )
shows that the monitoring data is very accurate but the
message overhead is very high for a short time interval,
e.g., T min ¼ 250 ms. However, at a large time interval, e.g.,
T min ¼ 2000 ms, the message overhead is very low but
the monitoring data error is very high. Thus, the monitoring accuracy increases at the cost of increased network
overhead.
OpenTM [94] is a query-based monitoring method to
estimate the traffic matrix (TM) for OF networks. OpenTMs
logic is quite simple. It keeps tracking all the active flows in
the network, gets the routing information from the OF
controllers routing application, discovers flow paths, and
periodically polls flow byte and packet-count counters
from switches on the flow path. Using the routing information, OpenTM constructs the TM by adding up statistics for
flows originated from the same source and destined to the
same destination. It is similar to FlowSense to compute utilization with a low overhead in the OF network. However,
by measuring network-wide traffic matrix by periodically
polling one switch on each flow’s path for collecting flow
level statistics, it cause a significant overhead. By polling
method that randomly selects some switches, it may affect
accuracy if the switch is not carefully chosen.
FlowSense [95], in contrast to the on-demand approach
used in OpenTM [94], is a passive push-based monitoring
method to analyze control messages between the controller and switches. It uses the controller messages to monitor
21
and measure network utilization such as the bandwidth
consumed by flows traversing the link, without inducing
additional overhead. For example, FlowSense uses the
PacketIn and FlowRemoved messages in OF networks to
estimate per flow link utilization. The evaluation results
show that FlowSense has high accuracy compared with
the Polling method and can accomplish 90% link utilization estimating jobs under 10 s based on a small testbed
consisting of two OF switches and one controller.
OpenSketch [96] is a software defined traffic measurement architecture, which separates the measurement data
plane from the control plane. OpenSketch provides a simple three-stage pipeline (hashing, filtering, and counting)
at switches, which can be implemented with commodity
switch components and support many measurement tasks.
OpenSketch is both generic and efficient to allow more
customized operations and thus can realize more efficient
data collection with respect to choosing which flow to
measure by using both hashing and wildcard rules. In the
control plane, OpenSketch provides a measurement library
that automatically configures the pipeline and allocates
resource for different measurement tasks. The three-stage
pipeline is implemented on NetFPGA hard-ware as an OF
switch. The OpenSketch library includes a list of sketches,
the sketch manager, and the resource allocator. Sketches
can be used for many measurement programs such as
heavy hitters [99,100], the traffic change detection [101],
the flow size distribution estimation [102], the global iceberg detection [103], and the fine-grained delay measurement [104]. Thus, OpenSketch makes measurement
programming easier at the controller. The monitoring
framework similar to the above solutions are also proposed
in [105] where a monitoring frame-work utilizes secondary controllers to identify and monitor aggregate flows
using a small set of rules that changes dynamically with
traffic load. This framework monitors only a large aggregate of flows instead of monitoring all flows as PayLess
[34]. This monitoring framework is based on wildcard rules
(at switches) that match one bit in the packet header, and
includes a hierarchical heavy hitter (HHH) algorithm [100]
in order to achieve high accuracy with low monitoring
overhead. A framework is proposed in [106], which can
instruct hash-based switches for collecting traffic information, along with the HHH algorithm for defining important
traffic, to support different measurement tasks with tradeoffs between accuracy and overhead.
MicroTE [37] is a fine-grained traffic engineering
scheme that works atop a variety of underlying data center
network topologies. It has a monitoring component in the
server, instead of letting the network controller periodically poll switches. Their solutions directly provide advantages allowing proactively respond to the changes in the
traffic load, scale down a large network, and reduce the
processing overhead imposed by the MicroTE on the network devices. This server-based system offers these advantages via the following approaches: (1) it allows the
controller to receive triggered updates of traffic loads,
especially when the traffic loads change significantly,
while a purely switch based approach, at least in the current implementation of OF, only supports polling by the
controller, which is far less flexible; (2) it prevents the
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
network controller from creating a significant amount of
control traffic on the network by constantly polling all
switches on nearly a per second granularity; and (3) it
shifts the bottleneck of constantly generating flow statistics from the switches to the end hosts. Each of the servers
in the monitoring components tracks the network traffic
being sent/received over its interfaces as well as with
whom these bytes were exchanged. However, only one server per rack is responsible for aggregating, processing, and
summarizing the network statistics for the entire rack. This
server, called the designated server, is also in charge of
sending the summarized traffic matrix to the network controller. To fulfill its role, the designated server must be able
to perform the following tasks: (1) collect data from other
servers in the rack, (2) aggregate the server to server data
into Rack to Rack data, (3) determine predictable ToR pairs
(i.e., pairs of Top-of-Rack switches), and (4) communicate
this information with the network controller.
OpenSample [97] proposed by IBM research lab, is a
sampling-based SDN measurement system, which uses a
packet sampling tool, sFlow [36], to capture packet header
samples from the network with low overhead and uses TCP
sequence numbers from the captured headers to measure
accurate flow statistics. Using these two methods (packet
samples and TCP sequence numbers) OpenSample extracts
flow statistics for detecting elephant flows, estimating port
utilization at each switch, generating a snapshot of the
network state for use to other applications, and enabling
traffic engineering. Thus, OpenSample achieves the lowlatency measurements with high accuracy by using the
sFlow with TCP sequence numbers rather than using the
expensive OF rules, such that the counter function in OF
switches for each flow table, flow entry, port, queue, group,
group bucket, meter and meter band may be implemented
in software and maintained by polling hardware mechanisms [1]. Moreover, the implementation of OpenSample
does not need to modify the end-host server that is
required by MicroTE [37].
7.2. Checking network invariants
The verification of the network invariants is an important task in SDN networks. SDN will simplify the development of network applications, but bugs are likely to remain
problematic since the complexity of the software will
increase [107]. Moreover, SDN allows multiple applications
or even multiple users to program the same physical network simultaneously, potentially resulting in conflicting
rules that alter the intended behavior of one or more applications [107,108].
VeriFlow [107] is a verification tool in order to achieve
a real-time checking in SDN networks. It employs a similar
concept of the real-time network policy checking from
[93,92]. It is designed as a proxy residing between the controller and switches in the network for monitoring all communication in either direction and verifying network-wide
invariant violations dynamically as each forwarding rule is
inserted. The verification latency should be within a few
milliseconds to achieve real-time responses according to
[43] because the current SDN controllers are capable of
handling around 30 K new flow installs per second while
maintaining a sub-10 ms flow installation time. To implement VeriFlow with high speeds for every rule insertion
or deletion in the forwarding table at each switch, VeriFlow
slices the network into a set of equivalence classes of
packets based on the destination IP address with a longest-prefix-match rule, which will only affect the forwarding of the packets destined for that prefix. By employing
such approach, it is shown that network invariants can
be verified within hundred of microseconds as new rules
are installed into the network.
OFRewind [109] runs as a proxy on the substrate control channel between the controller and switches to enable
recording and replay of events for troubleshooting
problems in production networks.
FlowChecker [110] deploys the similar ideas in ConfigChecker [111] which uses Binary Decision Diagrams
(BDDs) to analyze the end-to-end access control configuration of all network devices (such as routers, firewalls, IPSec
gateways, NAT and multicasting), FlowChecker allows OF
administrators/users to manually verify the consistency
of multiple controllers and switches across different OF
federated infrastructure. For example, it verifies a configuration of network rules such as forwarding rules in the
forwarding tables by using Boolean expression to detect
misconfigurations.
7.3. Debugging programming errors
Modern networks provide a variety of interrelated services including routing, traffic monitoring, load balancing,
and access control. Unfortunately, the languages used to
program today’s networks lack some modern features.
They are usually defined at the low level of abstraction
supplied by the underlying hardware and they fail to provide even rudimentary support for modular programming.
As a result, network programs tend to be complicated,
error-prone, and difficult to maintain [112].
NICE [113] is an efficient and systematically technique
tool, which is a combination of model checking and
symbolic execution to efficiently discover violations of network-wide correctness properties due to bugs in the
controller programs. By using NICE, the OF programmer
can be instructed to check for generic correctness properties such as forwarding loops, black holes, or applicationspecific correctness properties, etc. The programming
model checking tool relies on the controller program as a
set of event handlers, a switch program as the values of
all variables for defining the switch state and identifying
transitions, and an end host program such as client/server
or mobile user. NICE is implemented by using Python language to seamlessly support OF controller programs. Thus,
NICE performs symbolic execution to explore all program
code paths through an entire OF network.
ndb [114] is a debugging software tool, which allows
SDN programmers and operators, to track down the root
cause of a bug. The ndb network debugger inspired by
gdb provides breakpoint and breaktrace keywords along
with a packet backtrace function, which allows to define
a packet breakpoint (e.g., an un-forwarded packet or a
packet filter), and then shows the sequence of information
relevant to code path, events, and inputs regarding a
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forwarding packet. Thus, ndb can find bugs in any level in
the SDN stack and it provides an idealized model better
than NICE [113].
7.4. Open research issues
Traffic Analysis: To achieve the potential benefits of the
SDN-TE, there are still open challenges based on the following critical issues. The traffic analysis of the SDN-TE
is significantly dependent on how global information
related to application or traffic characteristics and
states can be obtained in close-to real time fashion.
Moreover, the global information can be obtained from
3G/4G cellular networks that have a tremendous
growth of mobile data access and bandwidth usage.
Thus, how to efficiently handle the bigdata with regard
to user behavior, locality, and time-dependent statistics,
is the major consideration in the developing SDN-TE. In
[56], in-depth traffic pattern analysis method was
presented as a bigdata analysis. According to [56], the
bigdata analysis solution should include the parallel
data mining method such as like the K-means clustering
algorithm for analyzing a large volume of traffic data.
Thus, the effective parallel data mining not only enables
the extraction of various statistics, but also significantly
speeds up the whole process. Such a combination of
traffic analysis and data mining methods also makes it
possible to derive more general conclusions about
smartphone usage patterns.
Traffic monitoring: The open challenge issues in traffic
monitoring mechanisms are regarding how to reduce
significant network overhead when SDN controller(s)
or the monitoring device collects the network statistics
with high accuracy.
Network invariant checking and programming error
debugging methods: The verification and debugging
methods should work together with network security
issues. How to early detect or prevent intrusions by
using verification network or programming error
checking methods is largely unexploited. The security
mechanisms are out of scope areas of traffic engineering. Therefore, we did not account it in this survey
studies.
8. Existing TE tools for SDN-OpenFlow networks
8.1. Industry solutions
Here we present the state of the art of TE tools for SDNs
in the industry, which is summarized in Table 8.
B4 [115] designed by Google, is a Software Defined
WAN for Googles data center networks. The centralized
traffic engineering is applied to easily allocate bandwidth
among competing services based on application priority,
dynamically shifting communication patterns, and prevailing failure conditions. They address the critical performance and reliability issues that Wide Area Networks
(WANs) faced when delivering terabits per second of
Table 8
Qualitative overview of existing industrial TE tools for SDN-OpenFlow networks.
Industry solutions
Proposed
approaches
Description
Traffic engineering technology
Analysis
B4 [115] from
Google
A Software Defined WAN for Google’s
data center networks.
Using the centralized Traffic Engineering
(CTE) to adjudicate among competing
resource demands, measure available network capacity for multi-path forwarding/
tunneling, and dynamically reallocate bandwidth from the link or switch failures.
Using hash-based ECMP algorithm for loadbalancing.
Near 100% link utilization for
the majority of the links and
70% link utilization overall.
SWAN [116]
from
Microsoft
A Software-driven WAN (SWAN) for
inter-data center WANs.
Using two types of sharing policies: (a) the
different ranking classes of traffic, and (b)
the same priority of traffic with the max–
min fairness principle.
98% of the maximum allowed
network throughput, compared
with 60% in MPLS-enable WAN.
Dynamic
routing
for SDN
[2] from
Bell Labs
An optimized routing control algorithm
for SDN.
Using the Fully Polynomial Time Approximation Scheme (FPTAS) to solve the SDN
controller optimization problem that minimize the maximum utilization of the links
in the network.
FPTAS based routing outperforms a standard OSPF routing
in SDNs.
ADMCFSNOS
[84,49,50]
from Huawei
An integrated resource control and
management system for large centrally
controlled or loosely coupled distributed
network systems.
Using the Adaptive Dynamic Multi-path
Computation Framework (ADMCF) to provide the necessary infrastructure and algorithms for data collection, analysis, and
various optimization algorithms.
Using Static and Dynamic Hybrid Routing
(SDHR) algorithms for computing the optimal routing to provide a simpler and more
resource efficient near-optimal hybrid routing than destination-bead or explicit routing
schemes.
SDHR based routing outperforms
an explicit routing about 95%
TCAM space saved at the normalized throughput about 70%.
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
aggregate bandwidth across thousands of individual links.
Conventionally, WAN links can only achieve 30—40% average utilization and may also experience unexpected link
failures. To encounter such problem, B4 is designed based
on SDN principles and OF [1] to manage individual
switches. The core part is the centralized Traffic Engineering (CTE), which allows adjustment among competing
resource demands, measuring available network capacity
for multi-path forwarding/tunneling, and dynamically
reallocating bandwidth from the link/switch failures. The
CTE architecture includes the network topology graph that
represents sites as nodes, and site to site connectivity as
edge switches. Using this graph, the aggregate traffic is
computed at site–site edges, and then the abstract computed results are fed into the TE Optimization Algorithm
for fairly allocating bandwidth among all Flow Group
(FG), where FG can be generated for the individual application and represented by a tuple (including source site, destination site, QoS). The Tunnel (call) represents a site-level
path in the networks, such as a sequence of connecting
nodes for a path. The Tunnel Group (TG) maps FGs to a
set of tunnels according to the weights that specify the
fraction of FG traffic to be forwarded along each tunnel.
For load-balance routing, B4 uses a hash-based ECMP algorithm. By employing the above schemes, B4 has managed
to achieve high link utilization. For example, it is reported
that in B4, all links have average 70% link utilization for
long time periods (such as 24-h), while many links are running under 99% of the bandwidth utilization.
SWAN [116] is a Software-driven WAN (SWAN) proposed by Microsoft, which utilizes policy rules to allow
inter-data center WANs to carry significantly more traffic
for higher-priority services, while maintaining fairness
among similar services. Conventionally, WAN is operated
using MPLS TE based on ECMP routing, which can spread
traffic across a number of tunnels between ingress-egress
router pairs. However, this approach yields very low efficiency due to the lack of global view at edge router/
switches. In this case, greedy resource allocation has to
be performed for a flow by using the shortest path with
available capacity (CSPF). To solve the above problems,
SWAN exploits the global network view enabled by the
SDN paradigm to optimize the network sharing polices,
which allows WAN to carry more traffic and support flexible network-wide sharing. More specifically, two types
of sharing policies are employed. First, a small number of
traffic classes, e.g., interactive traffic, elastic traffic, and
background traffic, are ranked according to their priorities,
and then the network resources are allocated among the
traffic flows based on their priorities. Second, the traffic
flows with the same priority are allocated with the
network resource according to the max–min fairness principle. As a consequence, SWAN carries about 98% of the
maximum allowed network traffic, while MPLS-enable
WAN only carries around 60%.
Dynamic routing for SDN [2] proposed at Bell Labs,
addresses the routing optimization problem using the
Fully Polynomial Time Approximation Scheme (FPTAS).
The optimization problem aims to find the optimal routes
for network traffic flows such that delay and packet loss
at the links are minimized, thus define how to minimize
the maximum utilization of the links in the network. Specifically, FPTAS in [2] solves the dynamic routing problem
instead of a standard linear programming problem. FPTAS
is very simple to implement and runs significantly faster
than a general linear programming solver. The algorithms
are implemented as a SDN routing on ns-2 simulator
[117]. Compared with a standard OSPF routing, it shows
that the proposed SDN routing outperforms OSPF routing
in terms of overall network throughput, delay and packet
loss rate.
ADMCF-SNOS [49,50], the Adaptive Dynamic Multipath Computation Framework for Smart Network Operating Systems (ADMCF-SNOS) from Shannon Lab of Huawei
utilizes its Smart Network Operating System (SNOS) is to
provide an integrated resource control and management
system for large centrally controlled or loosely coupled
distributed network systems. The management applications are built on top of the Smart OF Controller
(SOX)[48] enhanced by dynamic resource-oriented APIs.
One of such an application was the Adaptive Dynamic
Multi-path Computation Framework (ADMCF). ADMCF
[49] was designed as an open and easily extensible
solution framework that can provide the necessary infrastructure and algorithms for data collection, analysis, and
various optimization algorithms. The ADMCF designers
believed that it would be impossible for any single optimization algorithm to get satisfactory solutions for a large
centrally controlled network while its topology, states,
and more critically application traffic can change rapidly.
Instead, a set of algorithms that work together in an
adaptive and intelligent fashion would be more capable
to provide a more adequate global routing and resource
allocation optimization. As it would be costly for the central optimization algorithms to calculate good routes
dynamically, such a framework should take advantage of
many hidden patterns in the combinations of network
topology, states, and traffic flows.
ADMCF consists of four main components: (1) Routing
Policy & Rule Configuration – Administrator or Network OS
specify and configure various policies and rules based on
global network information, client/application QoS requirements, traffic statistics and Patterns, etc. (2) Adaptive &
Dynamic Multi-Path Computation – Innovative combination
of enhanced edge-disjoint path algorithms with iterative
CSPF algorithm, and/or other heuristics that can truly
perform global optimization. (3) Path Evaluator/Assessor –
a mechanism that can take into account of contributing factors in the evaluation and selection of paths obtained from
above set of algorithms. (4) Path DB – selecting proper paths
and update the Path DB.
Static and Dynamic Hybrid Routing (SDHR) [118] [119]
Classical TE methods calculate the optimal routing based
on a known traffic matrix. However, it is very difficult to
get an accurate traffic matrix in a large operational network because of frequent changes of service demands.
Thus, it is of interest to find a set of good routing configuration to accommodate for a wide range of traffic matrices
and offers the near optimality of performance for each such
traffic matrix. SDHR intended to provide a simpler and
more resource efficient near-optimal hybrid routing solution than destination-based or explicit routing. For any
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
25
Table 9
Qualitative overview of existing academic TE tools for SDN-OpenFlow networks.
Academic solutions
Proposed approaches
Description
Traffic engineering technology
Plug-n-Serve [120] from Stanford University
An OpenFlow enabled web
server load-balancing
application.
An OpenFlow enabled loadbalancing application.
An OpenFlow enabled loadbalancing application.
Uses the LOBUS algorithm for flow management so it can add/delete servers to
unstructured network for traffic adjustments.
Aster⁄x [121] from Stanford
University
OpenFlow-based load balancer
[42] from Princeton University
FlowVisor [122] from Stanford
University
A network virtualization tool.
Enhanced version of the Plug-n-Serve to manage a large network of switches
and servers for minimizing the average response time of web services.
Using wildcard rules, switches can handle ‘‘microflows’’ without involving the
controller.
It is a proxy protocol to sit between the multiple controllers and the switches in
order to allow multiple controllers to share the same network infrastructure
without interfering with each other.
specific traffic demands, it would adapt to some ‘‘best’’ suited routing decisions. Its hybrid routing achieves load balancing for multiple traffic matrices, by complementing
destination-based routing with a small number of explicit
routing decisions to take advantage of both approaches.
Hybrid routing greatly reduces the number of forwarding
entries and thus requires less TCAM resources. For the four
test networks frequently used for such comparison and the
two 500 nodes randomly generated subnetworks, SDHR
demonstrated near-optimal load balancing improvements
on ‘‘normalized throughput’’ from 35% to over 70%, while
saving up to 95% TCAM resources compared to explicit
routing. The approach is to pre-compute a basic destination-based routing and multiple sets of complementary
explicit routing, and then dynamically apply different set
of explicit routing to achieve load balancing for a wide
range of traffic matrices, according to traffic changes.
The OF-enabled or compliant switches can easily support such combination of destination-based routing and
explicit routing in their forwarding tables with the centralized controller. The controller can install both destinationbased routing entries and multiple sets of explicit routing
entries in the flow tables, while at any given time, only
the set of explicit routing that ‘‘best matches’’ the current
traffic patterns is active. In hybrid routing, if a packet
matches both active explicit routing and destination-based
routing entries, the active explicit routing entry will take
precedence to forward the packet. The multi-path forwarding with ADMCF and its algorithms were successfully
tested at EANTC [84].
8.2. Academic solutions
Here we present the TE tools for SDNs in the academia,
which are summarized in Table 9.
Plug-n-Serve [120] developed at Stanford University, is
an OF-enabled load balancing application for web traffic. It
tries to minimize response time by controlling the load on
the network and the servers using customized flow routing. It operates in an unstructured network topology.
Plug-n-Serve can add new servers to unstructured network, detect the changes and make traffic adjustments
that minimize the response time. Plug-n-Serve uses LOBUS
(LOad-Balancing over UnStructured networks) algorithm
for Flow Manager which is effective in adding servers to
network such that LOBUS automatically expands its view
of the network and appropriately shares the load over
the added devices.
Aster⁄x[121] also developed at Stanford University is an
improved version of Plug-n-Serve [120] and can be used at
a much larger scale network. Aster⁄x is a server load-balancing system that effectively minimize the average
response time of web services in unstructured networks
built with cheap commodity hardware. Using OF to keep
track of state and to control the routes allows the system
to be easily reconfigured; the network operator, thus, can
add or remove capacity by turning hosts on or off, and
add or remove path diversity by turning switches on or
off. In addition, the system allows operators to increase
the capacity of the web service by simply plugging in computing resources and switches in an arbitrary manner.
Aser⁄x load-balancing system has three functional units:
Flow Manager for the OF controller that manages and
routes flows based on the specific load-balancing algorithm chosen. Net Manager probes the network and keeps
track of the network topology and its utilization levels. It
queries switches periodically to get link usage and monitor
the latency experienced by packets traversing the links.
Host Manager monitors the state and load at individual
servers in the system, and reports the collected information to the Flow Manager. By employing above schemes,
the SDN controller of Aser⁄x system is capable of managing
a large network of switches and servers.
OpenFlow-based load balancer [42] developed at
Princeton University, is an efficient load-balancing architecture, which includes the partitioning algorithm for generating wildcard rules that are proactively installed into the
switches to direct requests for ‘‘microflows’’ without involving the controller. More specifically, by this load-balancing
approach, the switch performs an ‘‘action’’ of rewriting the
server IP address and forwarding the packet to the output
port associated with the chosen replica server by using the
wildcard rules.
FlowVisor [122] originally developed at Stanford
University and continued in ON.LAB [123]. FlowVisor is a
network virtualization application which can be considered as a proxy protocol to sit logically between the multiple controllers and the OF switches in order to allow
multiple controllers to share the same network infrastructure without interfering with each other. Since the main
purpose of FlowVisor is to provide virtualization in OF
networks, it does not provide many traffic engineering
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I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
mechanisms. In particular, it can allocate the link bandwidth by assigning a minimum data rate to the set of flows
that make up a slice, and also divides the flow-table in each
switch by keeping track of which flow-entries belong to
which controller. For handling new flow messages, when
a packet arrives at a switch that does not match an entry
in the flow table, a new flow message is sent to the controller. When there are multiple controllers, the new flow
requests may occur too frequently. To process these flows
on a switch with limited TCMA memory, the FlowVisor
tracks the new flow messages arrival rate for each slice,
and if it exceeds given threshold, the FlowVisor will insert
a forwarding rule to drop the problem packets for a short
period. Therefore, the FlowVisor needs a specific TE for
supporting significant flows for a special controller among
the multiple controllers to define routes depending on the
different priority of traffic.
9. Conclusions
In this paper, we provide an overview of traffic
engineering mechanisms in SDN architectures. We study
the traditional traffic engineering technologies from early
ideas in ATM networking through current developments
in IP and MPLS networking. In particular, we investigate
the traffic management with regard to load balancing, fault
tolerance, consistent network update methods, as well as
traffic analysis for testing and debugging network systems
and network monitoring tools. We cover important
network aspects of availability, scalability, reliability, and
consistency in data networking with SDN. Moreover, we
study the traffic engineering mechanisms and describe
how to apply them to SDN/OF networks. SDN is a fastevolving research area in data networking with open
research issues. For availability and scalability issues,
SDN-TE system should manage data flow efficiently at both
the control plane and the data plane with the tradeoffs
between latency and load-balance. For reliability issues,
in the data plane, fast failure recovery mechanisms should
be implemented with low-overhead communications
between the controller and the switches. In the control
plane, the fault tolerance mechanisms must consider a single point failure and should define an optimal number of
controllers and the best location of controllers for the primary control and the backup controller(s) with a tradeoff
between reliability and latencies of a variety of traffic patterns in the entire network. For consistency issues, the SDN
controller efficiently updates the network with consistency
in real-time and safety without packet drops, and with low
synchronization overhead. Thus, SDN’s effectiveness and
great potential for next generation data networking come
with many new technical challenges, which need to be
addressed by the new research advances.
Acknowledgment
The authors would like to thank Caterina Scoglio,
Mehmet Can Vuran, Eylem Ekici, and Xudong Wang, for
their valuable comments and suggestions to improve the
quality of the paper.
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Pu Wang received the B.E. degree in Electrical
Engineering from Beijing Institute of Technology, China, in 2003, and the M.E. degree in
Electrical and Computer Engineering from
Memorial University of Newfoundland, Canada, in 2008. He received the Ph.D. degree in
Electrical and Computer Engineering from the
Georgia Institute of Technology, Atlanta, GA
USA, in August 2013, under the guidance of
Prof. Dr. Ian F. Akyildiz. Currently, he is an
Assistant Professor with the Department of
Electrical Engineering and Computer Science
at the Wichita State University. He received the Broadband Wireless
Networking Laboratory (BWN Lab) Researcher of the Year Award at the
Georgia Institute of Technology in 2012. He received the TPC top ranked
paper award of IEEE DySPAN 2011. He was also named Fellow of the
School of Graduate Studies, Memorial University of Newfoundland in
2008. He is a member of the IEEE. His current research interests are
wireless sensor networks, cognitive radio networks, software defined
networking, Internet of multimedia things, nanonetworks, and wireless
communications in challenged environment.
Ian F. Akyildiz received the B.S., M.S., and
Ph.D. degrees in Computer Engineering from
the University of ErlangenNrnberg, Germany,
in 1978, 1981 and 1984, respectively. Currently, he is the Ken Byers Chair Professor in
Telecommunications with the School of Electrical and Computer Engineering, Georgia
Institute of Technology (Georgia Tech),
Atlanta, GA USA; the Director of the Broadband Wireless Networking (BWN) Laboratory
and the Chair of the Telecommunication
Group at Georgia Tech. Since 2013, he is a
FiDiPro Professor (Finland Distinguished Professor Program (FiDiPro)
supported by the Academy of Finland) in the Department of Electronics
and Communications Engineering, at Tampere University of Technology,
Finland, and the founding director of NCC (Nano Communications Center). Since 2008, he is also an honorary professor with the School of
Electrical Engineering at Universitat Politcnica de Catalunya (UPC) in
Barcelona, Catalunya, Spain, and the founding director of N3Cat (NaNoNetworking Center in Catalunya). Since 2011, he is a Consulting Chair
Professor at the Department of Information Technology, King Abdulaziz
University (KAU) in Jeddah, Saudi Arabia. He is the Editor-in-Chief of
Computer Networks (Elsevier) Journal, and the founding Editor-in-Chief
of the Ad Hoc Networks (Elsevier) Journal, the Physical Communication
(Elsevier) Journal and the Nano Communication Networks (Elsevier)
Journal. He is an IEEE Fellow (1996) and an ACM Fellow (1997). He
received numerous awards from IEEE and ACM. His current research
interests are in nanonetworks, Terahertz Band communication networks,
Long Term Evolution Advanced (LTE-A) networks, cognitive radio networks and wireless sensor networks.
Min Luo received the Ph.D. degree in Electrical Engineering from Georgia Institute of
Technology, Atlanta, GA USA, in 1992. He also
held the B.S., and M.S. degrees in 1982 and
1987, respectively in Computer Science. Currently, he is the Head and Chief Architect of
the Advanced Networking at Huawei’s Shannon (IT) Lab, leading the research and development in Software Defined Networking
(SDN) and other future networking initiatives.
He served as Chief/Executive Architect for IBM
SWG’s Strategy and Technology, Global Business Solution CenterGCG, Industry Solutions, and Center of Excellence for
Enterprise Architecture and SOA for more than 11 years. He also worked
as Senior Operations Research Analyst, Senior Manager and Director of
Transportation Network Planning and Technologies for two Fortune 500
companies for 7 Years. He’s certified and awarded as the Distinguished
Lead/Chief Architect from Open Group in 2008. He is an established
expert in the field of next negation software defined networking (SDN),
enterprise architecture and information systems, whole life cycle software application and product development, business intelligence, and
business process optimization. He is also a pioneer and one of the recognized leading experts and educators in Service-oriented architecture
(SOA), Model/business-driven architecture and development (MDA-D),
and component/object-oriented technologies. He coauthored 2 books,
including the pioneering Patterns: Service Oriented Architecture and Web
Services in 2004, and published over 20 research papers. As a senior
member of IEEE, he has been serving on the organizing committee for
IEEEs ICWS, SCC and CC (Cloud Computing) Conferences, chaired sessions,
presented several tutorials on SOA and Enterprise Architecture and their
best practices and gave lectures at the Service University. He has served
as adjunct professors in several USA and Chinese universities since 1996.
Ahyoung Lee received the M.S., and Ph.D.
degrees in Computer Science and Engineering
from the University of Colorado, Denver, CO
USA in 2006 and 2011, respectively, and B.S.
degree in Information and Computer Engineering from the Hansung University in 2001,
Seoul, Korea. She was a Senior Researcher in
the Communication Policy Research Center at
the Yonsei University, Seoul, Korea in 2012.
Currently, she is a Postdoctoral Fellow at the
Georgia Institute of Technology, in the
Broadband Wireless Networking Laboratory
(BWN Lab) under the supervision of Prof. Dr. Ian F. Akyildiz with a
research project focused on Software Defined Networking (SDN). Her
main research interests include adaptive routing schemes for large-scale
network resources, analytical models and network performance evaluations in Ad Hoc Wireless Networks, Sensor Networks and Mobile Wireless
Networks; future internet architecture for wireless/mobile cloud networking; securing wireless applications and networks.
Wu Chou received the Ph.D. degree with four
advanced degrees in Science and Engineering
from the Stanford University, CA USA in 1990.
Currently, he is VP, Chief IT Scientist, and
Global Head of Huawei Shannon (IT) Lab, USA.
He is an IEEE Fellow, a renowned expert in the
field of IT, computing, networking, Internet/
Web, Big Data, SDN (software-defined-network), communication, signal processing,
speech and natural language processing,
machine learning, unified communication,
smart systems and endpoints. He has over 20+
years of professional career in leading R&D organizations. Before joining
Huawei, he was Director of R&D at Avaya. He joined AT&T Bell Labs after
obtaining his Ph.D. degree and continued his professional career from
AT&T Bell Labs to Lucent Bell Labs and Avaya Labs before joining Huawei.
In his role at Huawei, he leads the global Huawei Shannon (IT) Lab in its
30
I.F. Akyildiz et al. / Computer Networks 71 (2014) 1–30
research and innovation in the fast moving IT area. He has extensive
experience in cutting-edge technology research, incubating ground
breaking products, visionary technical leadership, and agile execution in
research and product development. He published over 150 journal and
conference papers, holds 32 USA and international patents with many
additional patent applications pending. He received Bell Laboratories
Presidents Gold Award for his achievement in 1997, Avaya Leadership
Award in 2005, and the outstanding standard and patent contribution
award in 2008 and 2009. He is a well known figure in standard bodies and
professional societies. He served as an editor for multiple standards at
W3C, ECMA, ISO, ETSI, etc. He was an editor of IEEE Transactions on
Services Computing (TSC), IEEE TSC Special Issue on Cloud Computing,
IEEE Transaction on Audio and Language Processing, and Journal of Web
Services Research.
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