Red Hat OpenStack Platform 9 Architecture Guide

Red Hat OpenStack Platform 9 Architecture Guide
Red Hat OpenStack Platform
9
Architecture Guide
Introduction to the product, components, and architectural examples
OpenStack Team
Red Hat OpenStack Platform 9 Architecture Guide
Introduction to the product, components, and architectural examples
OpenStack Team
rhos-docs@redhat.com
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Abstract
This guide introduces the OpenStack cloud components and provides design guidelines and
architecture examples to help you design your own OpenStack cloud.
Table of Contents
Table of Contents
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3. . . . . . . . . .
PREFACE
.CHAPTER
. . . . . . . . .1.. .COMPONENTS
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4. . . . . . . . . .
1.1. NETWORKING
6
1.2. STORAGE
8
1.3. VIRTUAL MACHINES, IMAGES, AND TEMPLATES
14
1.4. IDENTITY MANAGEMENT
24
1.5. USER INTERFACES
26
1.6. THIRD-PARTY COMPONENTS
30
.CHAPTER
. . . . . . . . .2.. .NETWORKING
. . . . . . . . . . . . .IN-DEPTH
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32
...........
2.1. HOW BASIC NETWORKING WORKS
32
2.2. NETWORKING IN OPENSTACK
33
2.3. CHOOSING A NETWORK BACKEND
33
2.4. ADVANCED OPENSTACK NETWORKING CONCEPTS
35
.CHAPTER
. . . . . . . . .3.. .DESIGN
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37
...........
3.1. PLANNING MODELS
37
3.2. COMPUTE RESOURCES
3.3. STORAGE RESOURCES
38
43
3.4. NETWORK RESOURCES
3.5. PERFORMANCE
48
53
3.6. MAINTENANCE AND SUPPORT
3.7. AVAILABILITY
3.8. SECURITY
54
55
56
3.9. ADDITIONAL SOFTWARE
3.10. PLANNING TOOL
57
58
. . . . . . . . . .4.. .ARCHITECTURE
CHAPTER
. . . . . . . . . . . . . . EXAMPLES
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59
...........
4.1. OVERVIEW
4.2. GENERAL-PURPOSE ARCHITECTURE
59
60
4.3. COMPUTE-FOCUSED ARCHITECTURE
4.4. STORAGE-FOCUSED ARCHITECTURES
63
69
4.5. NETWORK-FOCUSED ARCHITECTURES
76
. . . . . . . . . .5.. .DEPLOYMENT
CHAPTER
. . . . . . . . . . . . .INFORMATION
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85
...........
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Red Hat OpenStack Platform 9 Architecture Guide
2
PREFACE
PREFACE
Red Hat OpenStack Platform provides the foundation to build a private or public Infrastructure-as-aService (IaaS) cloud on top of Red Hat Enterprise Linux. It offers a highly scalable, fault-tolerant
platform for the development of cloud-enabled workloads.
Red Hat OpenStack Platform is packaged so that available physical hardware can be turned into a
private, public, or hybrid cloud platform that includes:
Fully distributed object storage
Persistent block-level storage
Virtual machine provisioning engine and image storage
Authentication and authorization mechanisms
Integrated networking
Web browser-based interface accessible to users and administrators
Note
For reference information about the components mentioned in this guide, see
Chapter 5, Deployment Information.
For the complete Red Hat OpenStack Platform documentation suite, see Red Hat
OpenStack Platform Documentation.
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Red Hat OpenStack Platform 9 Architecture Guide
CHAPTER 1. COMPONENTS
The Red Hat OpenStack Platform IaaS cloud is implemented as a collection of interacting services
that control compute, storage, and networking resources. The cloud can be managed with a webbased dashboard or command-line clients, which allow administrators to control, provision, and
automate OpenStack resources. OpenStack also has an extensive API, which is also available to all
cloud users.
The following diagram provides a high-level overview of the OpenStack core services and their
relationship with each other.
The following table describes each component shown in the diagram and provides links for the
component documentation section.
Table 1.1. Core services
4
Service
Code
Description
Location
Dashboard
horizon
Web browser-based dashboard that you
use to manage OpenStack services.
Section 1.5.1,
“OpenStack
Dashboard
(horizon)”
Identity
keystone
Centralized service for authentication and
authorization of OpenStack services and for
managing users, projects, and roles.
Section 1.4.1,
“OpenStack Identity
(keystone)”
CHAPTER 1. COMPONENTS
Service
Code
Description
Location
OpenStack
Networking
neutron
Provides connectivity between the
interfaces of OpenStack services.
Section 1.1.1,
“OpenStack
Networking
(neutron)”
Block Storage
cinder
Manages persistent block storage volumes
for virtual machines.
Section 1.2.1,
“OpenStack Block
Storage (cinder)”
Compute
nova
Manages and provisions virtual machines
running on hypervisor nodes.
Section 1.3.1,
“OpenStack
Compute (nova)”
Image
glance
Registry service that you use to store
resources such as virtual machine images
and volume snapshots.
Section 1.3.3,
“OpenStack Image
(glance)”
Object
Storage
swift
Allows users to store and retrieve files and
arbitrary data.
Section 1.2.2,
“OpenStack Object
Storage (swift)”
Telemetry
ceilometer
Provides measurements of cloud
resources.
Section 1.5.2,
“OpenStack
Telemetry
(ceilometer)”
Orchestration
heat
Template-based orchestration engine that
supports automatic creation of resource
stacks.
Section 1.3.4,
“OpenStack
Orchestration
(heat)”
Each OpenStack service contains a functional group of Linux services and other components. For
example, the glance-api and glance-registry Linux services, together with a MariaDB database,
implement the Image service. For information about third-party components included in OpenStack
services, see Section 1.6.1, “Third-party Components”.
Additional services are:
Section 1.3.2, “OpenStack Bare Metal Provisioning (ironic)” - Enables users to provision physical
machines (bare metal) with a variety of hardware vendors.
Section 1.2.3, “OpenStack Database-as-a-Service (trove)” - Enables users to deploy relational
and non-relational database engines and handles complex database administration tasks.
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Red Hat OpenStack Platform 9 Architecture Guide
Section 1.3.5, “OpenStack Data Processing (sahara)” - Enables users to provision and manage
Hadoop clusters on OpenStack.
1.1. NETWORKING
1.1.1. OpenStack Networking (neutron)
OpenStack Networking handles creation and management of a virtual networking infrastructure in
the OpenStack cloud. Infrastructure elements include networks, subnets, and routers. You can also
deploy advanced services such as firewalls or virtual private networks (VPN).
OpenStack Networking provides cloud administrators with flexibility to decide which individual
services to run on which physical systems. All service daemons can be run on a single physical host
for evaluation purposes. Alternatively, each service can have a unique physical host or replicated
across multiple hosts to provide redundancy.
Because OpenStack Networking is software-defined, it can react in real-time to changing network
needs, such as creation and assignment of new IP addresses.
OpenStack Networking advantages include:
Users can create networks, control traffic, and connect servers and devices to one or more
networks.
Flexible networking models can adapt to the network volume and tenancy.
IP addresses can be dedicated or floating, where floating IPs can be used for dynamic traffic
rerouting.
If using VLAN networking, you can use a maximum of 4094 VLANs (4094 networks), where 4094
= 2^12 (minus 2 unusable) network addresses, which is imposed by the 12-bit header limitation.
If using VXLAN tunnel-based networks, the VNI (Virtual Network Identifier) can use a 24-bit
header, which will essentially allow around 16 million unique addresses/networks.
Table 1.2. OpenStack Networking components
6
Component
Description
Network agent
Service that runs on each OpenStack node to perform local networking
configuration for the node virtual machines and for networking services such as
Open vSwitch.
neutron-dhcp-agent
Agent that provides DHCP services to tenant networks.
neutron-ml2
Plug-in that manages network drivers and provides routing and switching
services for networking services such as Open vSwitch or Ryu networks.
CHAPTER 1. COMPONENTS
Component
Description
neutron-server
Python daemon that manages user requests and exposes the Networking API.
The default server configuration uses a plug-in with a specific set of
networking mechanisms to implement the Networking API.
Certain plug-ins, such as the openvswitch and linuxbridge plug-ins, use
native Linux networking mechanisms, while other plug-ins interface with
external devices or SDN controllers.
neutron
Command-line client to access the API.
The placement of OpenStack Networking services and agents depends on the network
requirements. The following diagram shows an example of a common deployment model without a
controller. This model utilizes a dedicated OpenStack Networking node and tenant networks.
The example shows the following Networking service configuration:
Two Compute nodes run the Open vSwitch (ovs-agent), and one OpenStack Networking node
performs the following network functions:
L3 routing
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Red Hat OpenStack Platform 9 Architecture Guide
DHCP
NAT including services such as FWaaS and LBaaS
The compute nodes have two physical network cards each. One card handles tenant traffic, and
the other card manages connectivity.
The OpenStack Networking node has a third network card dedicated to provider traffic.
1.2. STORAGE
Section 1.2.1, “OpenStack Block Storage (cinder)”
Section 1.2.2, “OpenStack Object Storage (swift)”
Section 1.2.3, “OpenStack Database-as-a-Service (trove)”
1.2.1. OpenStack Block Storage (cinder)
OpenStack Block Storage provides persistent block storage management for virtual hard drives.
Block Storage enables the user to create and delete block devices, and to manage attachment of
block devices to servers.
The actual attachment and detachment of devices is handled through integration with the Compute
service. You can use regions and zones to handle distributed block storage hosts.
You can use Block Storage in performance-sensitive scenarios, such as database storage or
expandable file systems. You can also use it as a server with access to raw block-level storage.
Additionally, you can take volume snapshots to restore data or to create new block storage volumes.
Snapshots are dependent on driver support.
OpenStack Block Storage advantages include:
Creating, listing and deleting volumes and snapshots.
Attaching and detaching volumes to running virtual machines.
Although the main Block Storage services, such as volume, scheduler, API, can be co-located in a
production environment, it is more common to deploy multiple instances of the volume service along
one or more instances of the API and scheduler services to manage them.
Table 1.3. Block Storage components
8
Component
Description
openstack-cinder-api
Responds to requests and places them in the message queue. When a
request is received, the API service verifies that identity requirements are
met and translates the request into a message that includes the required
block storage action. The message is then sent to the message broker for
processing by the other Block Storage services.
CHAPTER 1. COMPONENTS
Component
Description
openstack-cinder-backup
Backs up a Block Storage volume to an external storage repository. By
default, OpenStack uses the Object Storage service to store the backup.
You can also use Ceph or NFS back ends as storage repositories for
backups.
openstack-cinder-scheduler
Assigns tasks to the queue and determines the provisioning volume
server. The scheduler service reads requests from the message queue
and determines on which block storage host to perform the requested
action. The scheduler then communicates with the openstack-cindervolume service on the selected host to process the request.
openstack-cinder-volume
Designates storage for virtual machines. The volume service manages
the interaction with the block-storage devices. When requests arrive from
the scheduler, the volume service can create, modify, or remove
volumes. The volume service includes several drivers to interact with the
block-storage devices, such as NFS, Red Hat Storage, or Dell
EqualLogic.
cinder
Command-line client to access the Block Storage API.
The following diagram shows the relationship between the Block Storage API, the scheduler, the
volume services, and other OpenStack components.
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Red Hat OpenStack Platform 9 Architecture Guide
1.2.2. OpenStack Object Storage (swift)
Object Storage provides an HTTP-accessible storage system for large amounts of data, including
static entities such as videos, images, email messages, files, or VM images. Objects are stored as
binaries on the underlying file system along with metadata stored in the extended attributes of each
file.
The Object Storage distributed architecture supports horizontal scaling as well as failover
redundancy with software-based data replication. Because the service supports asynchronous and
eventual consistency replication, you can use it in a multiple data-center deployment.
OpenStack Object Storage advantages include:
Storage replicas maintain the state of objects in case of outage. A minimum of three replicas is
recommended.
Storage zones host replicas. Zones ensure that each replica of a given object can be stored
separately. A zone might represent an individual disk drive, an array, a server, a server rack, or
even an entire data center.
Storage regions can group zones by location. Regions can include servers or server farms that
are usually located in the same geographical area. Regions have a separate API endpoint for
each Object Storage service installation, which allows for a discrete separation of services.
Object Storage uses ring .gz files, which serve as database and configuration files. These files
contain details of all the storage devices and mappings of stored entities to the physical location of
each file. Therefore, you can use ring files to determine the location of specific data. Each object,
account, and container server has a unique ring file.
10
CHAPTER 1. COMPONENTS
The Object Storage service relies on other OpenStack services and components to perform actions.
For example, the Identity Service (keystone), the rsync daemon, and a load balancer are all
required.
Table 1.4. Object Storage components
Component
Description
openstack-swift-account
Handles listings of containers with the account database.
openstack-swift-container
Handles listings of objects that are included in a specific container with the
container database.
openstack-swift-object
Stores, retrieves, and deletes objects.
openstack-swift-proxy
Exposes the public API, provides authentication, and routes requests.
Objects are streamed through the proxy server to the user without
spooling.
swift
Command-line client to access the Object Storage API.
Table 1.5. Object Storage housekeeping components
Housekee
ping
Auditing
Components
openstack-swift-account-auditor
openstack-swift-container-auditor
Description
Verifies the integrity of Object Storage
accounts, containers, and objects, and
helps to protect against data corruption.
openstack-swift-object-auditor
Replication
openstack-swift-account-replicator
openstack-swift-container-replicator
Ensures consistent and available
replication throughout the Object Storage
cluster, including garbage collection.
openstack-swift-object-replicator
Updating
openstack-swift-account-updater
Identifies and retries failed updates.
openstack-swift-container-updater
openstack-swift-object-updater
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Red Hat OpenStack Platform 9 Architecture Guide
The following diagram shows the main interfaces that the Object Storage uses to interact with other
OpenStack services, databases, and brokers.
1.2.3. OpenStack Database-as-a-Service (trove)
Warning
DEPRECATION NOTICE: Beginning in Red Hat OpenStack Platform 10, the OpenStack
Trove service will no longer be included in the Red Hat OpenStack Platform distribution.
We are working with a trusted partner to provide our customers with a production ready
DBaaS service. Please contact your sales account manager to learn more about this
option.
Note
This feature is available in this release as a Technology Preview, and therefore is not
fully supported by Red Hat. It should only be used for testing, and should not be deployed
in a production environment. For more information about Technology Preview features,
see Scope of Coverage Details.
OpenStack Database-as-a-Service allows users to select, provision, and operate a variety of
relational and non-relational databases and handles more complex database administration tasks
out-of-the-box.
12
CHAPTER 1. COMPONENTS
OpenStack Database-as-a-Service advantages include:
Users and database administrators can provision and manage multiple database instances in the
cloud.
High-performance resource isolation while automating complex administrative tasks such as
deployment, configuration, patching, backup, restore, and monitoring.
Table 1.6. Trove components
Component
Description
openstack-trove-api
RESTful API that supports JSON and XML to provision and manage Databaseas-a-Service instances.
openstack-troveconductor
Runs on the host to receive messages from guest instances with requests to
update information on the host. The requests can include the status of an
instance or the current status of a backup.
With openstack-trove-conductor, guest instances do not need a direct
connection to the host database. The service listens for RPC messages
through the message bus and performs the requested operation.
openstack-troveguestagent
Runs on the guest instance to manage and perform operations on the host
database directly. The openstack-trove-guestagent listens for RPC messages
through the message bus and performs the requested operation.
openstack-trovetaskmanager
Responsible for tasks such as provisioning instances, managing the lifecycle
of instances, and operations on the database instance.
trove
Command-line client to access the Database-as-a-Service API.
The following diagram shows the relationship between Database-as-a-Service and the other
OpenStack services.
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Red Hat OpenStack Platform 9 Architecture Guide
Important
Although OpenStack Database-as-a-Service is available from the default OpenStack
channels, you must manually install and configure the component.
1.3. VIRTUAL MACHINES, IMAGES, AND TEMPLATES
Section 1.3.1, “OpenStack Compute (nova)”
Section 1.3.2, “OpenStack Bare Metal Provisioning (ironic)”
Section 1.3.3, “OpenStack Image (glance)”
Section 1.3.4, “OpenStack Orchestration (heat)”
Section 1.3.5, “OpenStack Data Processing (sahara)”
1.3.1. OpenStack Compute (nova)
OpenStack Compute serves as the core of the OpenStack cloud by providing virtual machines on
demand. Compute schedules virtual machines to run on a set of nodes by defining drivers that
interact with underlying virtualization mechanisms, and by exposing the functionality to the other
OpenStack components.
14
CHAPTER 1. COMPONENTS
Compute supports the libvirt driver libvirtd that uses KVM as the hypervisor. The hypervisor creates
virtual machines and enables live migration from node to node. To provision bare metal machines,
you can also use Section 1.3.2, “OpenStack Bare Metal Provisioning (ironic)”.
Compute interacts with the Identity service to authenticate instance and database access, with the
Image service to access images and launch instances, and with the dashboard service to provide
user and administrative interface.
You can restrict access to images by project and by user, and specify project and user quota, such
as the number of instances that can be created by a single user.
When you deploy a Red Hat OpenStack Platform cloud, you can break down the cloud according to
different categories:
Regions
Each service cataloged in the Identity service is identified by the service region, which
typically represents a geographical location, and the service endpoint. In a cloud with
multiple compute nodes, regions enable discrete separation of services.
You can also use regions to share infrastructure between Compute installations while
maintaining a high degree of failure tolerance.
Cells (Technology Preview)
The Compute hosts can be partitioned into groups called cells to handle large deployments
or geographically separate installations. Cells are configured in a tree, where the top-level
cell, called the API cell, runs the nova-api service but no nova-compute services.
Each child cell in the tree runs all other typical nova-* services but not the nova-api service.
Each cell has a separate message queue and database service, and also runs the novacells service that manages the communication between the API cell and the child cells.
The benefits of cells include:
You can use a single API server to control access to multiple Compute installations.
An additional level of scheduling at the cell level is available that, unlike host scheduling,
provides greater flexibility and control when you run virtual machines.
Note
This feature is available in this release as a Technology Preview, and therefore is not
fully supported by Red Hat. It should only be used for testing, and should not be deployed
in a production environment. For more information about Technology Preview features,
see Scope of Coverage Details.
Host Aggregates and Availability Zones
A single Compute deployment can be partitioned into logical groups. You can create
multiple groups of hosts that share common resources such as storage and network, or
groups that share a special property such as trusted computing hardware.
To administrators, the group is presented as a Host Aggregate with assigned compute
nodes and associated metadata. The Host Aggregate metadata is commonly used to
provide information for openstack-nova-scheduler actions, such as limiting specific flavors
or images to a subset of hosts.
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Red Hat OpenStack Platform 9 Architecture Guide
To users, the group is presented as an Availability Zone. The user cannot view the group
metadata or see the list of hosts in the zone.
The benefits of aggregates, or zones, include:
Load balancing and instance distribution.
Physical isolation and redundancy between zones, implemented with a separate power
supply or network equipment.
Labeling for groups of servers that have common attributes.
Separation of different classes of hardware.
Table 1.7. Compute components
16
Component
Description
openstack-nova-api
Handles requests and provides access to the Compute services, such as
booting an instance.
openstack-nova-cert
Provides the certificate manager.
openstack-novacompute
Runs on each node to create and terminate virtual instances. The compute
service interacts with the hypervisor to launch new instances, and ensures that
the instance state is maintained in the Compute database.
openstack-novaconductor
Provides database-access support for compute nodes to reduce security risks.
openstack-novaconsoleauth
Handles console authentication.
openstack-novanetwork
Network services that can serve as an alternative to OpenStack Networking
and handle basic network traffic for private and public access. For a
comparison of OpenStack Networking and Compute Networking, see
Chapter 2, Networking In-Depth .
openstack-novanovncproxy
Provides a VNC proxy for browsers to enable VNC consoles to access virtual
machines.
openstack-novascheduler
Dispatches requests for new virtual machines to the correct node based on
configured weights and filters.
CHAPTER 1. COMPONENTS
Component
Description
nova
Command-line client to access the Compute API.
The following diagram shows the relationship between the Compute services and other OpenStack
components.
1.3.2. OpenStack Bare Metal Provisioning (ironic)
OpenStack Bare Metal Provisioning enables the user to provision physical, or bare metal machines,
for a variety of hardware vendors with hardware-specific drivers. Bare Metal Provisioning integrates
with the Compute service to provision the bare metal machines in the same way that virtual
machines are provisioned, and provides a solution for the bare-metal-to-trusted-tenant use case.
OpenStack Baremetal Provisioning advantages include:
Hadoop clusters can be deployed on bare metal machines.
Hyperscale and high-performance computing (HPC) clusters can be deployed.
Database hosting for applications that are sensitive to virtual machines can be used.
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Red Hat OpenStack Platform 9 Architecture Guide
Bare Metal Provisioning uses the Compute service for scheduling and quota management, and uses
the Identity service for authentication. Instance images must be configured to support Bare Metal
Provisioning instead of KVM.
The following diagram shows how Ironic and the other OpenStack services interact when a physical
server is being provisioned:
Table 1.8. Bare Metal Provisioning components
Component
Description
openstack-ironic-api
Handles requests and provides access to Compute resources on the bare
metal node.
openstack-ironicconductor
Interacts directly with hardware and ironic databases, and handles requested
and periodic actions. You can create multiple conductors to interact with
different hardware drivers.
ironic
Command-line client to access the Bare Metal Provisioning API.
The Ironic API is illustrated in following diagram:
18
CHAPTER 1. COMPONENTS
1.3.3. OpenStack Image (glance)
OpenStack Image acts as a registry for virtual disk images. Users can add new images or take a
snapshot of an existing server for immediate storage. You can use the snapshots for backup or as
templates for new servers.
Registered images can be stored in the Object Storage service or in other locations, such as simple
file systems or external Web servers.
The following image disk formats are supported:
aki/ami/ari (Amazon kernel, ramdisk, or machine image)
iso (archive format for optical discs, such as CDs)
qcow2 (Qemu/KVM, supports Copy on Write)
raw (unstructured format)
vhd (Hyper-V, common for virtual machine monitors from vendors such as VMware, Xen,
Microsoft, and VirtualBox)
vdi (Qemu/VirtualBox)
vmdk (VMware)
Container formats can also be registered by the Image service. The container format determines the
type and detail level of the virtual machine metadata to store in the image.
The following container formats are supported:
bare (no metadata)
ova (OVA tar archive)
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Red Hat OpenStack Platform 9 Architecture Guide
ovf (OVF format)
aki/ami/ari (Amazon kernel, ramdisk, or machine image)
Table 1.9. Image components
Component
Description
openstack-glance-api
Interacts with storage back ends to handle requests for image retrieval
and storage. The API uses openstack-glance-registry to retrieve image
information. You must not access the registry service directly.
openstack-glance-registry
Manages all metadata for each image.
glance
Command-line client to access the Image API.
The following diagram shows the main interfaces that the Image service uses to register and
retrieve images from the Image database.
1.3.4. OpenStack Orchestration (heat)
OpenStack Orchestration provides templates to create and manage cloud resources such as
storage, networking, instances, or applications. Templates are used to create stacks, which are
collections of resources.
20
CHAPTER 1. COMPONENTS
For example, you can create templates for instances, floating IPs, volumes, security groups, or
users. Orchestration offers access to all OpenStack core services with a single modular template, as
well as capabilities such as auto-scaling and basic high availability.
OpenStack Orchestration advantages include:
A single template provides access to all underlying service APIs.
Templates are modular and resource-oriented.
Templates can be recursively defined and reusable, such as nested stacks. The cloud
infrastructure can then be defined and reused in a modular way.
Resource implementation is pluggable, which allows for custom resources.
Resources can be auto-scaled, and therefore added or removed from the cluster based on
usage.
Basic high availability functionality is available.
Table 1.10. Orchestration components
Component
Description
openstack-heat-api
OpenStack-native REST API that processes API requests by sending the
requests to the openstack-heat-engine service over RPC.
openstack-heat-api-cfn
Optional AWS-Query API compatible with AWS CloudFormation that
processes API requests by sending the requests to the openstack-heatengine service over RPC.
openstack-heat-engine
Orchestrates template launch and generates events for the API
consumer.
openstack-heat-cfntools
Package of helper scripts such as cfn-hup, which handle updates to
metadata and execute custom hooks.
heat
Command-line tool that communicates with the Orchestration API to
execute AWS CloudFormation APIs.
The following diagram shows the main interfaces that the Orchestration service uses to create a
new stack of two new instances and a local network.
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1.3.5. OpenStack Data Processing (sahara)
OpenStack Data Processing enables the provisioning and management of Hadoop clusters on
OpenStack. Hadoop stores and analyze large amounts of unstructured and structured data in
clusters.
Hadoop clusters are groups of servers that can act as storage servers running the Hadoop
Distributed File System (HDFS), compute servers running Hadoop’s MapReduce (MR) framework,
or both.
The servers in a Hadoop cluster need to reside in the same network, but they do not need to share
memory or disks. Therefore, you can add or remove servers and clusters without affecting
compatibility of the existing servers.
The Hadoop compute and storage servers are co-located, which enables high-speed analysis of
stored data. All tasks are divided across the servers and utilizes the local server resources.
OpenStack Data Processing advantages include:
Identity service can authenticate users and provide user security in the Hadoop cluster.
Compute service can provision cluster instances.
Image service can store cluster instances, where each instance contains an operating system
and HDFS.
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CHAPTER 1. COMPONENTS
Object Storage service can be used to store data that Hadoop jobs process.
Templates can be used to create and configure clusters. Users can change configuration
parameters by creating custom templates or overriding parameters during cluster creation.
Nodes are grouped together using a Node Group template, and cluster templates combine Node
Groups.
Jobs can be used to execute tasks on Hadoop clusters. Job binaries store executable code, and
data sources store input or output locations and any necessary credentials.
Data Processing supports the Cloudera (CDH) plug-in as well as vendor-specific management tools,
such as Apache Ambari. You can use the OpenStack dashboard or the command-line tool to
provision and manage clusters.
Table 1.11. Sahara components
Component
Description
openstack-sahara-all
Legacy package that handles API and engine services.
openstack-sahara-api
Handles API requests and provides access to the Data Processing services.
openstack-saharaengine
Provisioning engine that handles cluster requests and data delivery.
sahara
Command-line client to access the Data Processing API.
The following diagram shows the main interfaces that the Data Processing service uses to provision
and manage a Hadoop cluster.
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Red Hat OpenStack Platform 9 Architecture Guide
1.4. IDENTITY MANAGEMENT
1.4.1. OpenStack Identity (keystone)
OpenStack Identity provides user authentication and authorization to all OpenStack components.
Identity supports multiple authentication mechanisms, including user name and password
credentials, token-based systems, and AWS-style log-ins.
By default, the Identity service uses a MariaDB back end for token, catalog, policy, and identity
information. This back end is recommended for development environments or to authenticate
smaller user sets. You can also use multiple identity back ends concurrently, such as LDAP and
SQL. You can also use memcache or Redis for token persistence.
Identity supports Federation with SAML. Federated Identity establishes trust between Identity
Providers (IdP) and the services that Identity provides to the end user.
Note
Federated Identity and concurrent multiple back ends require Identity API v3 and Apache
HTTPD deployment instead of Eventlet deployment.
OpenStack Identity advantages include:
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CHAPTER 1. COMPONENTS
User account management, including associated information such as a name and password. In
addition to custom users, a user must be defined for each cataloged service. For example, the
glance user must be defined for the Image service.
Tenant, or project, management. Tenants can be the user group, project, or organization.
Role management. Roles determine the user permissions. For example, a role might
differentiate between permissions for a sales rep and permissions for a manager.
Domain management. Domains determine the administrative boundaries of Identity service
entities, and support multi-tenancy, where a domain represents a grouping of users, groups, and
tenants. A domain can have more than one tenant, and if you use multiple concurrent Identity
providers, each provider has one domain.
Table 1.12. Identity components
Component
Description
openstack-keystone
Provides Identity services, together with the administrative and public
APIs. Both Identity API v2 and API v3 are supported.
keystone
Command-line client to access the Identity API.
The following diagram shows the basic authentication flow that Identity uses to authenticate users
with other OpenStack components.
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1.5. USER INTERFACES
Section 1.5.1, “OpenStack Dashboard (horizon)”
Section 1.5.2, “OpenStack Telemetry (ceilometer)”
1.5.1. OpenStack Dashboard (horizon)
OpenStack Dashboard provides a graphical user interface for users and administrators to perform
operations such as creating and launching instances, managing networking, and setting access
control.
The Dashboard service provides the Project, Admin, and Settings default dashboards. The modular
design enables the dashboard to interface with other products such as billing, monitoring, and
additional management tools.
The following image shows an example of the Compute panel in the Admin dashboard.
The role of the user that logs in to the dashboard determines which dashboards and panels are
available.
Table 1.13. Dashboard components
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CHAPTER 1. COMPONENTS
Component
Description
openstack-dashboard
Django Web application that provides access to the
dashboard from any Web browser.
Apache HTTP server (httpd service)
Hosts the application.
The following diagram shows an overview of the dashboard architecture.
The example shows the following interaction:
The OpenStack Identity service authenticates and authorizes users
The session back end provides database services
The httpd service hosts the Web application and all other OpenStack services for API calls
1.5.2. OpenStack Telemetry (ceilometer)
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OpenStack Telemetry provides user-level usage data for OpenStack-based clouds. The data can be
used for customer billing, system monitoring, or alerts. Telemetry can collect data from notifications
sent by existing OpenStack components such as Compute usage events, or by polling OpenStack
infrastructure resources such as libvirt.
Telemetry includes a storage daemon that communicates with authenticated agents through a
trusted messaging system to collect and aggregate data. Additionally, the service uses a plug-in
system that you can use to add new monitors. You can deploy the API Server, central agent, data
store service, and collector agent on different hosts.
The service uses a MongoDB database to store collected data. Only the collector agents and the
API server have access to the database.
The alarms and notifications are newly handled and controlled by the aodh service.
Table 1.14. Telemetry components
28
Component
Description
openstack-aodh-api
Provides access to the alarm information stored in the data store.
openstack-aodh-alarmevaluator
Determines when alarms fire due to the associated statistic trend
crossing a threshold over a sliding time window.
openstack-aodh-alarmnotifier
Executes actions when alarms are triggered.
openstack-aodh-alarmlistener
Emits an alarm when a pre-defined event pattern occurs.
openstack-ceilometer-api
Runs on one or more central management servers to provide access to
data in the database.
openstack-ceilometer-central
Runs on a central management server to poll for utilization statistics
about resources independent from instances or Compute nodes. The
agent cannot be horizontally scaled, so you can run only a single
instance of this service at a time.
CHAPTER 1. COMPONENTS
Component
Description
openstack-ceilometercollector
Runs on one or more central management servers to monitor the
message queues. Each collector processes and translates notification
messages to Telemetry messages, and sends the messages back to the
message bus with the relevant topic.
Telemetry messages are written to the data store without modification.
You can choose where to run these agents, because all intra-agent
communication is based on AMQP or REST calls to the ceilometer-api
service, similar to the ceilometer-alarm-evaluator service.
openstack-ceilometercompute
Runs on each Compute node to poll for resource utilization statistics.
Each nova-compute node must have a ceilometer-compute agent
deployed and running.
openstack-ceilometernotification
Pushes metrics to the collector service from various OpenStack services.
ceilometer
Command-line client to access the Telemetry API.
The following diagram shows the interfaces used by the Telemetry service.
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Red Hat OpenStack Platform 9 Architecture Guide
1.6. THIRD-PARTY COMPONENTS
1.6.1. Third-party Components
Some Red Hat OpenStack Platform components use third-party databases, services, and tools.
1.6.1.1. Databases
MariaDB is the default database that is shipped with Red Hat Enterprise Linux. MariaDB enables
Red Hat to fully support open source community-developed software. Each OpenStack
component except Telemetry requires a running MariaDB service. Therefore, you need to deploy
MariaDB before you deploy a full OpenStack cloud service or before you install any standalone
OpenStack component.
The Telemetry service uses a MongoDB database to store collected usage data from collector
agents. Only the collector agents and the API server have access to the database.
1.6.1.2. Messaging
RabbitMQ is a robust open-source messaging system based on the AMQP standard. RabbitMQ is a
high-performance message broker used in many enterprise systems with widespread commercial
support. In Red Hat OpenStack Platform, RabbitMQ is the default and recommended message
broker.
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CHAPTER 1. COMPONENTS
RabbitMQ manages OpenStack transactions including queuing, distribution, security, management,
clustering, and federation. It also serves a key role in high availability and clustering scenarios.
1.6.1.3. External Caching
External applications for caching, such as memcached or Redis, offer persistence and shared
storage and speed up dynamic web applications by reducing the database load. External caching is
used by various OpenStack components, for example:
The Object Storage service uses memcached to cache authenticated clients, instead of requiring
each client to re-authorize each interaction.
By default, the dashboard uses memcached for session storage.
The Identity service uses Redis or memcached for token persistence.
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CHAPTER 2. NETWORKING IN-DEPTH
2.1. HOW BASIC NETWORKING WORKS
Networking consists of moving information from one computer to another. At the most basic level,
this is performed by running a cable between two machines, each with a network interface card
(NIC) installed. If you ever studied the OSI networking model, this is layer 1.
When you want to involve more than two computers in the conversation, you need to scale out this
configuration by adding a device called a switch. Switches are dedicated devices with multiple
Ethernet ports to which you connect additional machines. This configuration is called a Local Area
Network (LAN).
Switches move up the OSI model to layer 2 and apply more intelligence than the lower layer 1. Each
NIC has a unique MAC address that is assigned to the hardware, and this number allows machines
that are plugged in to the same switch find each other.
The switch maintains a list of which MAC addresses are plugged into which ports, so that when one
computer attempts to send data to another computer, the switch knows where each NIC is located
and adjusts the circuitry to direct the network traffic to the correct destination.
2.1.1. Connecting multiple LANs
If you use two LANs on two separate switches, you can connect them to share information with
each other in the following ways:
Trunk cable
You can connect the two switches directly with a physical cable, called a trunk cable. In this
configuration, you plug each end of the trunk cable into a port on each switch, and then
define these ports as trunk ports. Now the two switches act as one big logical switch, and
the connected computers can successfully find each other. This option is not very scalable,
and overhead becomes an issue the more switches you link directly.
Router
You can use a device called a router to plug in cables from each switch. As a result, the
router is aware of the networks that are configured on both switches. Each switch that you
plug into the router becomes an interface and is assigned an IP address, known as the
default gateway for that network. The "default" in default gateway means that this is the
destination where traffic will be sent if it is clear that the destination computer is not on the
same LAN as the source of the data transfer.
After you set this default gateway on each of your computers, they do not need to be aware
of all of the other computers on the other networks to send traffic to them. The traffic is just
sent to the default gateway and the router handles it from there. Since the router is aware of
which networks reside on which interface, it can send the packets on to their intended
destinations. Routing works at layer 3 of the OSI model, and utilizes familiar concepts like IP
addresses and subnets.
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Note
This concept is how the Internet itself works. Many separate networks run by different
organizations are all inter-connected using switches and routers. Keep following the
correct default gateways and your traffic will eventually get to where it needs to go.
2.1.2. VLANs
Virtual Local Area Networks (VLANs) allow you to segment network traffic for computers that run on
the same switch. You can logically divide your switch by configuring the ports to be members of
different networks. This configuration turns the ports into mini-LANs that allow you to separate traffic
for security purposes.
For example, if your switch has 24 ports, you can define ports 1-6 to belong to VLAN200, and ports
7-18 belong to VLAN201. Computers that are plugged into VLAN200 are completely separate from
computers on VLAN201, and they can no longer communicate directly. All traffic between the two
VLANs must now pass through the router as if they were two separate physical switches. You can
also enhance the security with firewalls to determine which VLANs can communicate with each
other.
2.1.3. Firewalls
Firewalls operate at the same OSI layer as IP routing. They are often located in the same network
segments as the routers, where they govern the traffic between all the networks. Firewalls use a
predefined set of rules that prescribe which traffic can or cannot enter a network. These rules can
be very granular. For example, you can define a rule where servers on VLAN 200 can only
communicate with computers on VLAN201, and only on a Thursday afternoon, and only if the traffic
is Web (HTTP) and moves in one direction.
To help enforce these rules, some firewalls also perform Stateful Packet Inspection (SPI), where
they examine the contents of packets to ensure that they are what they claim to be. Hackers are
known to exfiltrate data by sending traffic that masquerades as something else, and SPI is one
method that can help mitigate that threat.
2.1.4. Bridges
Network bridges are switches that operate at the same level 2 of the OSI model, but their only
function is to connect separate networks together, similar to routers.
2.2. NETWORKING IN OPENSTACK
All of the basic networking concepts in an OpenStack cloud, except that they are defined by services
and configuration. This is known as Software-Defined Networking (SDN). Virtual switches (Open
vSwitch) and routers (l3-agent) allow your instances to communicate with each other, and allow
them to communicate externally using the physical network. The Open vSwitch bridge allocates
virtual ports to instances and spans across to the physical network to allow incoming and outgoing
traffic.
2.3. CHOOSING A NETWORK BACKEND
Red Hat OpenStack Platform offers two distinctly different networking backends: Nova networking
(nova-network) and OpenStack Networking (neutron).
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Nova networking was deprecated in the OpenStack technology roadmap, but is still available.
OpenStack Networking is considered the core software-defined networking (SDN) component of the
OpenStack forward-looking roadmap and it is in active development.
Important
There is no migration path between Nova networking and OpenStack Networking.
Therefore, if you plan to deploy Nova networking and migrate to OpenStack Networking in
the future, you must manually migrate all networks and configurations. This migration is
likely to lead to network outage.
2.3.1. When to choose OpenStack Networking (neutron)
If you require an overlay network solution. OpenStack Networking supports GRE or VXLAN
tunneling for virtual machine traffic isolation. With GRE or VXLAN, no VLAN configuration is
required on the network fabric and the only requirement from the physical network is to provide
IP connectivity between the nodes. Furthermore, VXLAN or GRE allows a theoretical scale limit
of 16 million unique IDs which is much greater than the 4094 limitation of 802.1q VLAN ID. By
comparison, Nova networking bases the network segregation on 802.1q VLANs and does not
support tunneling with GRE or VXLAN.
If you require overlapping IP addresses between tenants. OpenStack Networking uses the
network namespace capabilities of the Linux kernel, which allows different tenants to use the
same subnet range, such as 192.168.100/24, on the same Compute node without any risk of
overlap or interference. This is recommended for large multi-tenancy deployments. By
comparison, Nova networking supports only flat topologies that must remain mindful of subnets
that are used by all tenants.
If you require a Red Hat-certified third-party OpenStack Networking plug-in. By default, Red Hat
Enterprise Linux OpenStack Platform 5 and later use the open source ML2 core plug-in with the
Open vSwitch (OVS) mechanism driver. Based on the physical network fabric and other network
requirements, third-party OpenStack Networking plug-ins can be deployed instead of the default
ML2/Open vSwitch driver.
Note
Red Hat is constantly working to enhance our Partner Certification Program to certify
more OpenStack Networking plugins against Red Hat OpenStack Platform. You can
learn more about our Certification Program and the certified OpenStack Networking
plug-ins at http://marketplace.redhat.com
If you require VPN-as-a-service (VPNaaS), Firewall-as-a-service (FWaaS), or Load-Balancingas-a-service (LBaaS). These network services are only available in OpenStack Networking and
are not available in Nova networking. The dashboard allows tenants to manage these services
without administrator intervention.
2.3.2. When to choose Nova networking (nova-network)
If your deployment requires flat (untagged) or VLAN (802.1q tagged) networking. This type of
deployment implies scalabilty requirements, since the theoretical scale limit of 4094 VLAN IDs is
typically higher than the limit of physical switches, as well as management and provisioning
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CHAPTER 2. NETWORKING IN-DEPTH
requirements. Specific configuration is necessary on the physical network to trunk the required
set of VLANs between the nodes.
If your deployment does not require overlapping IP addresses between tenants. This is usually
recommended only for small, private deployments.
If you do not need a software-defined networking (SDN) solution, or the ability to interact with the
physical network fabric.
If you do not need self-service VPN, firewall, or load-balancing services.
2.4. ADVANCED OPENSTACK NETWORKING CONCEPTS
2.4.1. Layer 3 High Availability
OpenStack Networking hosts virtual routers on a centralized Network node, which is a physical
server that is dedicated to the function of hosting the virtual networking components. These virtual
routers direct traffic to and from virtual machines, and are vital to the continued connectivity of your
environment. Since physical servers might experience outages due to many reasons, your virtual
machines might be vulnerable to outages when the Network node becomes unavailable.
OpenStack Networking uses Layer 3 High Availability to help mitigate this vulnerability,
implementing the industry standard VRRP to protect virtual routers and floating IP addresses. With
Layer 3 High Availability, the virtual routers of the tenant are randomly distributed across multiple
physical Network nodes, with one router designated as the active router, and the other routers on
standby, ready to take over if the Network node that hosts the active router experiences an outage.
Note
"Layer 3" refers to the section of the OSI model where this feature functions, and means
that it can protect routing and IP addressing.
For more information, see the "Layer 3 High Availability" section in the Networking Guide.
2.4.2. Load Balancing-as-a-Service (LBaaS)
Load Balancing-as-a-Service (LBaaS) enables OpenStack Networking to distribute incoming
network requests equally between designated instances. This distribution ensures the workload is
shared among instances and helps to use system resources more effectively. Incoming requests are
distributed using one of the following load balancing methods:
Round robin
Rotates requests evenly between multiple instances.
Source IP
Requests from a unique source IP address are always directed to the same instance.
Least connections
Allocates requests to the instance with the lowest number of active connections.
For more information, see the "Configuring Load Balancing-as-a-Service" section in the Networking
Guide.
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2.4.3. IPv6
OpenStack Networking supports IPv6 addresses in tenant networks, so you can dynamically assign
IPv6 addresses to virtual machines. OpenStack Networking can also integrate with SLAAC on your
physical routers, so that virtual machines can receive IPv6 addresses from your existing DHCP
infrastructure.
For more information, see the "IPv6" section in the Networking Guide.
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CHAPTER 3. DESIGN
CHAPTER 3. DESIGN
Section 3.1, “Planning Models”
Section 3.2, “Compute Resources”
Section 3.3, “Storage Resources”
Section 3.4, “Network Resources”
Section 3.5, “Performance”
Section 3.6, “Maintenance and Support”
Section 3.7, “Availability”
Section 3.8, “Security”
Section 3.9, “Additional Software”
Section 3.10, “Planning Tool”
This section describes technical and operational considerations to take when you design your Red
Hat OpenStack Platform deployment.
Note
All architecture examples in this guide assume that you deploy OpenStack Platform on
Red Hat Enterprise Linux 7.2 (or later) with the KVM hypervisor.
3.1. PLANNING MODELS
When you design a Red Hat OpenStack Platform deployment, the duration of the project can affect
the configuration and resource allocation of the deployment. Each planning model might aim to
achieve different goals, and therefore requires different considerations.
3.1.1. Short-term model (3 months)
To perform short-term capacity planning and forecasting, consider capturing a record of the
following metrics:
Total vCPU number
Total vRAM allocation
I/O mean latency
Network traffic
Compute load
Storage allocation
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The vCPU, vRAM, and latency metrics are the most accessible for capacity planning. With these
details, you can apply a standard second-order regression and receive a usable capacity estimate
covering the following three months. Use this estimate to determine whether you need to deploy
additional hardware.
3.1.2. Middle-term model (6 months)
This model requires review iterations and you must estimate deviations from the forecasting trends
and the actual usage. You can analyze this information with standard statistical tools or with
specialized analysis models such as Nash-Sutcliffe. Trends can also be calculated using secondorder regression.
Note
In deployments with multiple instance flavors, you can correlate vRAM and vCPU usage
more easily if you treat the vCPU and vRAM metrics as a single metric.
3.1.3. Long-term model (1 year)
Total resource usage can vary during one year, and deviations normally occur from the original longterm capacity estimate. Therefore, second-order regression might be an insufficient measure for
capacity forecasting, especially in cases where usage is cyclical.
When planning for a long-term deployment, a capacity-planning model based on data that spans
over a year must fit at least the first derivative. Depending on the usage pattern, frequency analysis
might also be required.
3.2. COMPUTE RESOURCES
Compute resources are the core of the OpenStack cloud. Therefore, it is recommended to consider
physical and virtual resource allocation, distribution, failover, and additional devices, when you
design your Red Hat OpenStack Platform deployment.
3.2.1. General considerations
Number of processors, memory, and storage in each hypervisor
The number of processor cores and threads directly affects the number of worker threads
that can run on a Compute node. Therefore, you must determine the design based on the
service and based on a balanced infrastructure for all services.
Depending on the workload profile, additional Compute resource pools can be added to the
cloud later. In some cases, the demand on certain instance flavors might not justify
individual hardware design, with preference instead given to commoditized systems.
In either case, initiate the design by allocating hardware resources that can service common
instances requests. If you want to add additional hardware designs to the overall
architecture, this can be done at a later time.
Processor type
Processor selection is an extremely important consideration in hardware design, especially
when comparing the features and performance characteristics of different processors.
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CHAPTER 3. DESIGN
Processors can include features specifically for virtualized compute hosts, such as
hardware-assisted virtualization and memory paging, or EPT shadowing technology. These
features can have a significant impact on the performance of your cloud VMs.
Resource nodes
You must take into account Compute requirements of non-hypervisor resource nodes in the
cloud. Resource nodes include the controller node and nodes that run Object Storage, Block
Storage, and Networking services.
Resource pools
Use a Compute design that allocates multiple pools of resources to be provided on-demand.
This design maximizes application resource usage in the cloud. Each resource pool should
service specific flavors of instances or groups of flavors.
Designing multiple resource pools helps to ensure that whenever instances are scheduled
to Compute hypervisors, each set of node resources is allocated to maximize usage of
available hardware. This is commonly referred to as bin packing.
Using a consistent hardware design across nodes in a resource pool also helps to support
bin packing. Hardware nodes selected for a Compute resource pool should share a
common processor, memory, and storage layout. Choosing a common hardware design
helps easier deployment, support and node lifecycle maintenance.
Over-commit ratios
OpenStack enables users to over-commit CPU and RAM on Compute nodes, which helps to
increase the number of instances that run in the cloud. However, over-committing can
reduce the performance of the instances.
The over-commit ratio is the ratio of available virtual resources compared to the available
physical resources.
The default CPU allocation ratio of 16:1 means that the scheduler allocates up to 16
virtual cores for every physical core. For example, if a physical node has 12 cores, the
scheduler can allocate up to 192 virtual cores. With typical flavor definitions of 4 virtual
cores per instance, this ratio can provide 48 instances on the physical node.
The default RAM allocation ratio of 1.5:1 means that the scheduler allocates instances to
a physical node if the total amount of RAM associated with the instances is less than 1.5
times the amount of RAM available on the physical node.
Tuning the over-commit ratios for CPU and memory during the design phase is important
because it has a direct impact on the hardware layout of your Compute nodes. When
designing a hardware node as a Compute resource pool to service instances, consider the
number of processor cores available on the node, as well as the required disk and memory
to service instances running at full capacity.
For example, an m1.small instance uses 1 vCPU, 20 GB of ephemeral storage, and 2,048
MB of RAM. For a server with 2 CPUs of 10 cores each, with hyperthreading turned on:
The default CPU overcommit ratio of 16:1 allows for 640 (2 × 10 × 2 × 16) total m1.small
instances.
The default memory over-commit ratio of 1.5:1 means that the server needs at least 853
GB (640 × 2,048 MB / 1.5) of RAM.
When sizing nodes for memory, it is also important to consider the additional memory
required to service operating system and service needs.
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3.2.2. Flavors
Each created instance is given a flavor, or resource template, which determines the instance size
and capacity. Flavors can also specify secondary ephemeral storage, swap disk, metadata to
restrict usage, or special project access. Default flavors do not have these additional attributes
defined. Instance flavors allow to measure capacity forecasting, because common use cases are
predictably sized and not sized ad-hoc.
To facilitate packing virtual machines to physical hosts, the default selection of flavors provides a
second largest flavor half the size of the largest flavor in every dimension. The flavor has half the
vCPUs, half the vRAM, and half the ephemeral disk space. Each subsequent largest flavor is half
the size of the previous flavor.
The following diagram shows a visual representation of flavor assignments in a general-purpose
computing design and for a CPU-optimized, packed server:
The default flavors are recommended for typical configurations of commodity server hardware. To
maximize utilization, you might need to customize the flavors or to create new flavors to align
instance sizes to available hardware.
If possible, limit your flavors to one vCPU for each flavor. It is important to note that Type 1
hypervisors can schedule CPU time more easily to VMs that are configured with one vCPU. For
example, a hypervisor that schedules CPU time to a VM that is configured with 4 vCPUs must wait
until four physical cores are available, even if the task to perform requires only one vCPU.
Workload characteristics can also influence hardware choices and flavor configuration, especially
where the tasks present different ratios of CPU, RAM, or HDD requirements. For information about
flavors, see Managing Instances and Images.
3.2.3. vCPU-to-physical CPU core ratio
The default allocation ratio in Red Hat OpenStack Platform is 16 vCPUs per physical, or
hyperthreaded, core.
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CHAPTER 3. DESIGN
The following table lists the maximum number of VMs that can be suitably run on a physical host
based on the total available memory, including 4GB reserved for the system:
Total RAM
VMs
Total vCPU
64GB
14
56
96GB
20
80
128GB
29
116
For example, planning an initial greenfields standup of 60 instances requires 3+1 compute nodes.
Usually, memory bottlenecks are more common than CPU bottlenecks. However, the allocation ratio
can be lowered to 8 vCPUs per physical core if needed.
3.2.4. Memory overhead
The KVM hypervisor requires a small amount of VM memory overhead, including non​-shareable
memory. Shareable memory for QEMU/KVM systems can be rounded to 200 MB for each
hypervisor.
vRAM
Physical memory usage (average)
256
310
512
610
1024
1080
2048
2120
4096
4180
Typically, you can estimate hypervisor overhead of 100mb per VM.
3.2.5. Over-subscription
Memory is a limiting factor for hypervisor deployment. The number of VMs that you can run on each
physical host is limited by the amount of memory that the host can access. For example, deploying
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a quad-core CPU with 256GB of RAM and more than 200 1GB instances leads to poor performance.
Therefore, you must carefully determine the optimal ratio of CPU cores and memory to distribute
across the instances.
3.2.6. Density
Instance density
In a compute-focused architecture, instance density is lower, which means that CPU and
RAM over-subscription ratios are also lower. You might require more hosts to support the
anticipated scale if instance density is lower, especially if the design uses dual-socket
hardware designs.
Host density
You can address the higher host count of dual-socket designs by using a quad-socket
platform. This platform decreases host density, which then increases the rack count. This
configuration can affect the network requirements, the number of power connections, and
might also impact the cooling requirements.
Power and cooling density
Reducing power and cooling density is an important consideration for data centers with
older infrastructure. For example, the power and cooling density requirements for 2U, 3U, or
even 4U server designs, might be lower than for blade, sled, or 1U server designs due to
lower host density.
3.2.7. Compute hardware
Blade servers
Most blade servers can support dual-socket, multi-core CPUs. To avoid exceeding the CPU
limit, select full-width or full-height blades. These blade types can also decrease server
density. For example, high-density blade servers, such as HP BladeSystem or Dell
PowerEdge M1000e, support up to 16 servers in only ten rack units. Half-height blades are
twice as dense as full-height blades, which results in only eight servers per ten rack units.
1U servers
1U rack-mounted servers that occupy only a single rack unit might offer higher server
density than a blade server solution. You can use 40 units of 1U servers in one rack to
provide space for the top of rack (ToR) switches. In comparison, you can only use 32 fullwidth blade servers in one rack.
However, 1U servers from major vendors have only dual-socket, multi-core CPU
configurations. To support a higher CPU configuration in a 1U rack-mount form factor,
purchase systems from original design manufacturers (ODMs) or second-tier manufacturers.
2U servers
2U rack-mounted servers provide quad-socket, multi-core CPU support, but with a
corresponding decrease in server density. 2U rack-mounted servers offer half of the density
that 1U rack-mounted servers offer.
Larger servers
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Larger rack-mounted servers, such as 4U servers, often provide higher CPU capacity and
typically support four or even eight CPU sockets. These servers have greater expandability,
but have much lower server density and are often more expensive.
Sled servers
Sled servers are rack-mounted servers that support multiple independent servers in a single
2U or 3U enclosure. These servers deliver higher density than typical 1U or 2U rackmounted servers.
For example, many sled servers offer four independent dual-socket nodes in 2U for a total
of eight CPU sockets. However, the dual-socket limitation on individual nodes might not be
sufficient to offset the additional cost and configuration complexity.
3.2.8. Additional devices
You might consider the following additional devices for Compute nodes:
Graphics processing units (GPUs) for high-performance computing jobs.
Hardware-based random number generators to avoid entropy starvation for cryptographic
routines. A random number generator device can be added to an instance using with the
instance image properties. /dev/random is the default entropy source.
SSDs for ephemeral storage to maximize read/write time for database management systems.
Host aggregates work by grouping together hosts that share similar characteristics, such as
hardware similarities. The addition of specialized hardware to a cloud deployment might add to
the cost of each node, so consider whether the additional customization is needed for all
Compute nodes, or just a subset.
3.3. STORAGE RESOURCES
When you design your cloud, the storage solution you choose impacts critical aspects of the
deployment, such as performance, capacity, availability, and interoperability.
Consider the following factors when you choose your storage solution:
3.3.1. General Considerations
Applications
Applications should be aware of underlying storage sub-systems to use cloud storage
solutions effectively. If natively-available replication is not available, operations personnel
must be able to configure the application to provide replication service.
An application that can detect underlying storage systems can function in a wide variety of
infrastructures and still have the same basic behavior regardless of the differences in the
underlying infrastructure.
I/O
Benchmarks for input-output performance provide a baseline for expected performance
levels. The benchmark results data can help to model behavior under different loads, and
help you to design a suitable architecture.
Smaller, scripted benchmarks during the lifecycle of the architecture can help to record the
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system health at different times. The data from the scripted benchmarks can assist to scope
and gain a deeper understanding of the organization needs.
Interoperability
Ensure that any hardware or storage management platform that you select is interoperable
with OpenStack components, such as the KVM hypervisor, which affects whether you can
use it for short-term instance storage.
Security
Data security design can focus on different aspects based on SLAs, legal requirements,
industry regulations, and required certifications for systems or personnel. Consider
compliance with HIPPA, ISO9000, or SOX based on the type of data. For certain
organizations, access control levels should also be considered.
3.3.2. OpenStack Object Storage (swift)
Availability
Design your object storage resource pools to provide the level of availability that you need
for your object data. Consider rack-level and zone-level designs to accommodate the
number of necessary replicas. The defult number of replicas is three. Each replica of data
should exist in a separate availability zone with independent power, cooling, and network
resources that service the specific zone.
The OpenStack Object Storage service places a specific number of data replicas as objects
on resource nodes. These replicas are distributed across the cluster based on a consistent
hash ring, which exists on all nodes in the cluster. In addition, a pool of Object Storage
proxy servers that provide access to data stored on the object nodes should service each
availability zone.
Design the Object Storage system with a sufficient number of zones to provide the minimum
required successful responses for the number of replicas. For example, if you configure
three replicas in the Swift cluster, the recommended number of zones to configure inside the
Object Storage cluster is five.
Although you can deploy a solution with fewer zones, some data may not be available and
API requests to some objects stored in the cluster might fail. Therefore, ensure you account
for the number of zones in the Object Storage cluster.
Object proxies in each region should leverage local read and write affinity so that local
storage resources facilitate access to objects wherever possible. You should deploy
upstream load balancing to ensure that proxy services are distributed across multiple zones.
In some cases, you might need third-party solutions to assist with the geographical
distribution of services.
A zone within an Object Storage cluster is a logical division, and can be comprised of a
single disk, a node, a collection of nodes, multiple racks, or multiple DCs. You must allow
the Object Storage cluster to scale while providing an available and redundant storage
system. You might need to configure storage policies with different requirements for
replicas, retention, and other factors that could affect the design of storage in a specific
zone.
Node storage
When designing hardware resources for OpenStack Object Storage, the primary goal is to
maximize the amount of storage in each resource node while also ensuring that the cost per
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terabyte is kept to a minimum. This often involves utilizing servers that can hold a large
number of spinning disks. You might use 2U server form factors with attached storage or
with an external chassis that holds a larger number of drives.
The consistency and partition tolerance characteristics of OpenStack Object Storage ensure
that data stays current and survives hardware faults without requiring specialized datareplication devices.
Performance
Object storage nodes should be designed so that the number of requests does not hinder
the performance of the cluster. The object storage service is a chatty protocol. Therefore,
using multiple processors with higher core counts ensures that the IO requests do not
inundate the server.
Weighting and cost
OpenStack Object Storage provides the ability to mix and match drives with weighting inside
the swift ring. When designing your swift storage cluster, you can use most cost-effective
storage solution.
Many server chassis can hold 60 or more drives in 4U of rack space. Therefore, you can
maximize the amount of storage for each rack unit at the best cost per terabyte. However, it
is not recommended to use RAID controllers in an object storage node.
Scaling
When you design your storage solution, you must determine the maximum partition power
required by the Object Storage service, which then determines the maximum number of
partitions that you can create. Object Storage distributes data across the entire storage
cluster, but each partition cannot span more than one disk. Therefore, the maximum number
of partitions cannot exceed the number of disks.
For example, a system with an initial single disk and a partition power of three can hold
eight (2 3 ) partitions. Adding a second disk means that each disk can hold four partitions.
The one-disk-per-partition limit means that this system cannot have more than eight disks
and limits its scalability. However, a system with an initial single disk and a partition power
of 10 can have up to 1024 (210) partitions.
Whenever you increase the system backend storage capacity, the partition maps
redistribute data across the storage nodes. In some cases, this replication consists of
extremely large data sets. In those cases, you should use backend replication links that do
not conflict with tenant access to data.
If more tenants begin to access data in the cluster and the data sets grow, you must add
front-end bandwidth to service data access requests. Adding front-end bandwidth to an
Object Storage cluster requires designing Object Storage proxies that tenants can use to
gain access to the data, along with the high availability solutions that enable scaling of the
proxy layer.
You should design a front-end load balancing layer that tenants and consumers use to gain
access to data stored within the cluster. This load balancing layer can be distributed across
zones, regions or even across geographic boundaries.
In some cases, you must add bandwidth and capacity to the network resources that service
requests between proxy servers and storage nodes. Therefore, the network architecture
that provides access to storage nodes and proxy servers should be scalable.
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3.3.3. OpenStack Block Storage (cinder)
Availability and Redundancy
The input-output per second (IOPS) demand of your application determines whether you
should use a RAID controller and which RAID level is required. For redundancy, you should
use a redundant RAID configuration, such as RAID 5 or RAID 6. Some specialized features,
such as automated replication of block storage volumes, might require third-party plug-ins or
enterprise block storage solutions to handle the higher demand.
In environments with extreme demand on Block Storage, you should use multiple storage
pools. Each device pool should have a similar hardware design and disk configuration
across all hardware nodes in that pool. This design provides applications with access to a
wide variety of Block Storage pools with various redundancy, availability, and performance
characteristics.
The network architecture should also take into account the amount of East-West bandwidth
required for instances to use available storage resources. The selected network devices
should support jumbo frames to transfer large blocks of data. In some cases, you might
need to create an additional dedicated backend storage network to provide connectivity
between instances and Block Storage resources to reduce load on network resources.
When you deploy multiple storage pools, you must consider the impact on the Block
Storage scheduler, which provisions storage across resource nodes. Ensure that
applications can schedule volumes in multiple regions with specific network, power, and
cooling infrastructure. This design allows tenants to build fault-tolerant applications
distributed across multiple availability zones.
In addition to the Block Storage resource nodes, it is important to design for high availability
and redundancy of APIs and related services that are responsible for provisioning and
providing access to the storage nodes. You should design a layer of hardware or software
load balancers to achieve high availability of the REST API services to provide
uninterrupted service.
In some cases, you might need to deploy an additional load balancing layer to provide
access to backend database services that are responsible for servicing and storing the state
of Block Storage volumes. You should design a highly-available database solution to store
the Block Storage databases, such as MariaDB and Galera.
Attached storage
The Block Storage service can take advantage of enterprise storage solutions using a plugin driver developed by the hardware vendor. A large number of enterprise plug-ins ship outof-the-box with OpenStack Block Storage, and others are available through third-party
channels.
General-purpose clouds typically use directly-attached storage in the majority of Block
Storage nodes. Therefore, you might need to provide additional levels of service to tenants.
These levels might only be provided by enterprise storage solutions.
Performance
If higher performance is needed, you can use high-performance RAID volumes. For
extreme performance, you can use high-speed solid-state drive (SSD) disks.
Pools
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Block Storage pools should allow tenants to choose appropriate storage solutions for their
applications. By creating multiple storage pools of different types and configuring an
advanced storage scheduler for the Block Storage service, you can provide tenants a large
catalog of storage services with a variety of performance levels and redundancy options.
Scaling
You can upgrade Block Storage pools to add storage capacity without interruption to the
overall Block Storage service. Add nodes to the pool by installing and configuring the
appropriate hardware and software. You can then configure the new nodes to report to the
proper storage pool with the message bus.
Because Block Storage nodes report the node availability to the scheduler service, when a
new node is online and available, tenants can use the new storage resources immediately.
In some cases, the demand on Block Storage from instances might exhaust the available
network bandwidth. Therefore, you should design the network infrastructure to service Block
Storage resources to allow you to add capacity and bandwidth seamlessly.
This often involves dynamic routing protocols or advanced networking solutions to add
capacity to downstream devices. The front-end and backend storage network designs
should include the ability to quickly and easily add capacity and bandwidth.
3.3.4. Storage Hardware
Capacity
Node hardware should support enough storage for the cloud services, and should ensure
that capacity can be added after deployment. Hardware nodes should support a large
number of inexpensive disks with no reliance on RAID controller cards.
Hardware nodes should also be capable of supporting high-speed storage solutions and
RAID controller cards to provide hardware-based storage performance and redundancy.
Selecting hardware RAID controllers that automatically repair damaged arrays assists with
the replacement and repair of degraded or destroyed storage devices.
Connectivity
If you use non-Ethernet storage protocols in the storage solution, ensure that the hardware
can handle these protocols. If you select a centralized storage array, ensure that the
hypervisor can connect to that storage array for image storage.
Cost
Storage can be a significant portion of the overall system cost. If you need vendor support, a
commercial storage solution is recommended but incurs a bigger expense. If you need to
minimize initial financial investment, you can design a system based on commodity
hardware. However, the initial saving might lead to increased running support costs and
higher incompatibility risks.
Directly-Attached Storage
Directly-attached storage (DAS) impacts the server hardware choice and affects host
density, instance density, power density, OS-hypervisor, and management tools.
Scalability
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Scalability is a major consideration in any OpenStack cloud. It can sometimes be difficult to
predict the final intended size of the implementation; consider expanding the initial
deployment in order to accommodate growth and user demand.
Expandability
Expandability is a major architecture factor for storage solutions. A storage solution that
expands to 50 PB is considered more expandable than a solution that only expands to 10
PB. This metric is different from scalability, which is a measure of the solution’s
performance as its workload increases.
For example, the storage architecture for a development platform cloud might not require
the same expandability and scalability as a commercial product cloud.
Fault tolerance
Object Storage resource nodes do not require hardware fault tolerance or RAID controllers.
You do not need to plan for fault tolerance in the Object Storage hardware, because the
Object Storage service provides replication between zones by default.
Block Storage nodes, Compute nodes, and cloud controllers should have fault tolerance
built-in at the hardware level with hardware RAID controllers and varying levels of RAID
configuration. The level of RAID should be consistent with the performance and availability
requirements of the cloud.
Location
The geographical location of instance and image storage might impact your architecture
design.
Performance
Disks that run Object Storage services do not need to be fast-performing disks. You can
therefore maximize the cost efficiency per terabyte for storage. However, disks that run
Block Storage services should use performance-boosting features that might require SSDs
or flash storage to provide high-performance Block Storage pools.
The storage performance of short-term disks that you use for instances should also be
considered. If Compute pools need high utilization of short-term storage, or requires very
high performance, you should deploy similar hardware solutions the solutions you deploy for
Block Storage.
Server type
Scaled-out storage architecture that includes DAS affects the server hardware selection.
This architecture can also affect host density, instance density, power density, OShypervisor, management tools, and so on.
3.3.5. Ceph Storage
If you consider Ceph for your external storage, the Ceph cluster backend must be sized to handle
the expected number of concurrent VMs with reasonable latency. An acceptable service level can
maintain 99% of I/O operations in under 20ms for write operations and in under 10ms for read
operations.
You can isolate I/O spikes from other VMs by configuring the maximum bandwidth for each Rados
Block Device (RBD) or by setting a minimum guaranteed commitment.
3.4. NETWORK RESOURCES
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3.4. NETWORK RESOURCES
Network availability is critical to the hypervisors in your cloud deployment. For example, if the
hypervisors support only a few virtual machines (VMs) for each node and your applications do not
require high-speed networking, then you can use one or two 1GB ethernet links. However, if your
applications require high-speed networking or your hypervisors support many VMs for each node,
one or two 10GB ethernet links are recommended.
A typical cloud deployment uses more peer​-to​-peer communication than a traditional core network
topology normally requires. Although VMs are provisioned randomly across the cluster, these VMs
need to communicate with each other as if they are on the same network. This requirement might
slow down the network and cause packet loss on traditional core network topologies, due to
oversubscribed links between the edges and the core of the network.
3.4.1. Segregate Your Services
OpenStack clouds traditionally have multiple network segments. Each segment provides access to
resources in the cloud to operators and tenants. The network services also require network
communication paths separated from the other networks. Segregating services to separate networks
helps to secure sensitive data and protects against unauthorized access to services.
The minimum recommended segragation involves the following network segments:
A public network segment used by tenants and operators to access the cloud REST APIs.
Normally, only the controller nodes and swift proxies in the cloud are required to connect to this
network segment. In some cases, this network segment might also be serviced by hardware load
balancers and other network devices.
An administrative network segment used by cloud administrators to manage hardware resources
and by configuration management tools to deploy software and services to new hardware. In
some cases, this network segment might also be used for internal services, including the
message bus and database services that need to communicate with each other.
Due to the security requirements for this network segment, it is recommended to secure this
network from unauthorized access. This network segment usually needs to communicate with
every hardware node in the cloud.
An application network segment used by applications and consumers to provide access to the
physical network and by users to access applications running in the cloud. This network needs
to be segregated from the public network segment and should not communicate directly with the
hardware resources in the cloud.
This network segment can be used for communication by Compute resource nodes and network
gateway services that transfer application data to the physical network outside of the cloud.
3.4.2. Choose Your Network Type
The network type that you choose plays a critical role in the design of the cloud network architecture.
OpenStack Networking (neutron) is the core software-defined networking (SDN) component of
the OpenStack forward-looking roadmap and is under active development.
Compute networking (nova-network) was deprecated in the OpenStack technology roadmap, but
is still currently available.
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Important
There is no migration path between Compute networking and OpenStack Networking.
Therefore, if you plan to deploy Compute networking, you cannot upgrade to OpenStack
Networking, and any migration between network types must be performed manually and
will require network outage.
3.4.2.1. When to choose OpenStack Networking (neutron)
Choose OpenStack Networking if you require any of the following functionality:
Overlay network solution. OpenStack Networking supports GRE and VXLAN tunneling for virtual
machine traffic isolation. GRE or VXLAN do not require VLAN configuration on the network fabric
and only require the physical network to provide IP connectivity between the nodes.
VXLAN or GRE also allow a theoretical scale limit of 16 million unique IDs, compared to the
4094 unique IDs limitation on a 802.1q VLAN. Compute networking bases the network
segregation on 802.1q VLANs and does not support tunneling with GRE or VXLAN.
Overlapping IP addresses between tenants. OpenStack Networking uses the network
namespace capabilities in the Linux kernel, which allows different tenants to use the same
subnet range, such as 192.168.100/24, on the same Compute node without the risk of overlap or
interference. This capability is appropriate for large multi-tenancy deployments.
By comparison, Compute networking offers only flat topologies that must remain aware of
subnets that are used by all tenants.
Red Hat-certified third-party OpenStack Networking plug-in. By default, Red Hat OpenStack
Platform uses the open source ML2 core plug-in with the Open vSwitch (OVS) mechanism
driver. The modular structure of OpenStack Networking allows you to deploy third-party
OpenStack Networking plug-ins instead of the default ML2/Open vSwitch driver based on the
physical network fabric and other network requirements.
Red Hat is expanding the Partner Certification Program to certify more OpenStack Networking
plug-ins for Red Hat OpenStack Platform. You can learn more about the Certification Program
and view a list of certified OpenStack Networking plug-ins at: http://marketplace.redhat.com
VPN-as-a-service (VPNaaS), Firewall-as-a-service (FWaaS), or Load-Balancing-as-a-service
(LBaaS). These network services are available only in OpenStack Networking and are not
available in Compute networking. The dashboard allows tenants to manage these services
without administrator intervention.
3.4.2.2. When to choose Compute Networking (nova-network)
The Compute networking (nova-network) service is primarily a layer-2 networking service that
functions in two modes. These modes differ by the VLAN usage:
Flat-network mode. All network hardware nodes and devices across the cloud are connected to a
single layer-2 network segment that provides access to application data.
VLAN segmentation mode. If network devices in the cloud support segmentation by VLANs,
each tenant in the cloud is assigned a network subnet mapped to a VLAN on the physical
network. If your cloud design needs to support multiple tenants and you want to use Compute
networking, you should use this mode.
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Compute networking is managed only by the cloud operator. Tenants cannot control network
resources. If tenants need to manage and create network resources, such as network segments and
subnets, you must install the OpenStack Networking service to provide network access to instances.
Choose Compute Networking in the following cases:
If your deployment requires flat untagged networking or tagged VLAN 802.1q networking. You
must keep in mind that networking topology restricts the theoretical scale to 4094 VLAN IDs, and
physical switches normally support a much lower number. This network also reduces
management and provisioning limits. You must manually configure the network to trunk the
required set of VLANs between the nodes.
If your deployment does not require overlapping IP addresses between tenants. This is usually
suitable only for small, private deployments.
If you do not need a software-defined networking (SDN) solution or the ability to interact with the
physical network fabric.
If you do not need self-service VPN, Firewall, or Load-Balancing services.
3.4.3. General Considerations
Security
Ensure that you segregate your network services and that traffic flows to the correct
destinations without crossing through unnecessary locations.
Consider the following example factors:
Firewalls
Overlay interconnects for joining separated tenant networks
Routing through or avoiding specific networks
The way that networks attach to hypervisors can expose security vulnerabilities. To mitigate
against exploiting hypervisor breakouts, separate networks from other systems and
schedule instances for the network to dedicated Compute nodes. This separation prevents
attackers from gaining access to the networks from a compromised instance.
Capacity planning
Cloud networks require capacity and growth management. Capacity planning can include
the purchase of network circuits and hardware with lead times that are measurable in
months or years.
Complexity
An complex network design can be difficult to maintain and troubleshoot. Although devicelevel configuration can ease maintenance concerns and automated tools can handle
overlay networks, avoid or document non-traditional interconnects between functions and
specialized hardware to prevent outages.
Configuration errors
Configuring incorrect IP addresses, VLANs, or routers can cause outages in areas of the
network or even in the entire cloud infrastructure. Automate network configurations to
minimize the operator error that can disrupt the network availability.
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Non-standard features
Configuring the cloud network to take advantage of vendor-specific features might create
additional risks.
For example, you might use multi-link aggregation (MLAG) to provide redundancy at the
aggregator switch level of the network. MLAG is not a standard aggregation format and
each vendor implements a proprietary flavor of the feature. MLAG architectures are not
interoperable across switch vendors, which leads to vendor lock-in and can cause delays or
problems when you upgrade network components.
Single Point of Failure
If your network has a Single Point Of Failure (SPOF) due to only one upstream link or only
one power supply, you might experience a network outage in the event of failure.
Tuning
Configure cloud networks to minimize link loss, packet loss, packet storms, broadcast
storms, and loops.
3.4.4. Networking Hardware
There is no single best-practice architecture for networking hardware to support an OpenStack cloud
that you can apply to all implementations. Key considerations for the selection of networking
hardware include:
Availability
To ensure uninterrupted cloud node access, the network architecture should identify any
single points of failure and provide adequate redundancy or fault-tolerance:
Network redundancy can be achieved by adding redundant power supplies or paired
switches.
For the network infrastructure, networking protocols such as LACP, VRRP or similar can
be used to achieve a highly available network connection.
To ensure that the OpenStack APIs and any other services in the cloud are highly
available, you should design a load-balancing solution within the network architecture.
Connectivity
All nodes within an OpenStack cloud require network connectivity. In some cases, nodes
require access to multiple network segments. The design must include sufficient network
capacity and bandwidth to ensure that all north-south and east-west traffic in the cloud have
sufficient resources.
Ports
Any design requires networking hardware that has the required ports:
Ensure you have the physical space required to provide the ports. A higher port density
is preferred, as it leaves more rack space for Compute or storage components.
Adequate port availability also prevents fault domains and assists with power density.
Higher density switches are more expensive and should also be considered, as it is
important to not to over-design the network if it is not required.
The networking hardware must support the proposed network speed. For example: 1
GbE, 10 GbE, or 40 GbE (or even 100 GbE).
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Power
Ensure that the physical data center provides the necessary power for the selected network
hardware. For example, spine switches in a leaf-and-spine fabric or end of row (EoR)
switches might not provide sufficient power.
Scalability
The network design should include a scalable physical and logical network design. Network
hardware should offer the types of interfaces and speeds that are required by the hardware
nodes.
3.5. PERFORMANCE
The performance of an OpenStack deployment depends on multiple factors that are related to the
infrastructure and controller services. User requirements can be divided to general network
performance, Compute resource performance, and storage systems performance.
Ensure that you retain a historical performance baseline of your systems, even when these systems
perform consistently with no slow-downs. Available baseline information is a useful reference when
you encounter performance issues and require data for comparison purposes.
In addition to Section 1.5.2, “OpenStack Telemetry (ceilometer)”, external software can also be used
to track performance. The Operational Tools repository for Red Hat OpenStack Platform includes
the following tools:
collectd
Graphite-web
InfluxDB
Grafana
3.5.1. Network Performance
The network requirements help to determine performance capabilities. For example, smaller
deployments might employ 1 Gigabit Ethernet (GbE) networking, and larger installations that serve
multiple departments or many users should use 10 GbE networking.
The performance of running instances might be limited by these network speeds. You can design
OpenStack environments that run a mix of networking capabilities. By utilizing the different interface
speeds, the users of the OpenStack environment can choose networks that fit their purposes.
For example, web application instances can run on a public network with OpenStack Networking
with 1 GbE capability, and the backend database can use an OpenStack Networking network with
10 GbE capability to replicate its data. In some cases, the design can incorporate link aggregation to
increase throughput.
Network performance can be boosted by implementing hardware load balancers that provide frontend services to the cloud APIs. The hardware load balancers can also perform SSL termination if
necessary. When implementing SSL offloading, it is important to verify the SSL offloading
capabilities of the selected devices.
3.5.2. Compute Nodes Performance
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Hardware specifications used in compute nodes including CPU, memory, and disk type, directly
affect the performance of the instances. Tunable parameters in the OpenStack services can also
directly affect performance.
For example, the default over-commit ratio for OpenStack Compute is 16:1 for CPU and 1.5 for
memory. These high ratios can lead to an increase in "noisy-neighbor" activity. You must carefully
size your Compute environment to avoid this scenario and ensure that you monitor your
environment when usage increases.
3.5.3. Block Storage Hosts Performance
Block Storage can use enterprise backend systems such as NetApp or EMC, scale-out storage
such as Ceph, or utilize the capabilities of directly-attached storage in the Block Storage nodes.
Block Storage can be deployed to enable traffic to traverse the host network, which could affect, and
be adversely affected by, the front-side API traffic performance. Therefore, consider using a
dedicated data storage network with dedicated interfaces on the controller and Compute hosts.
3.5.4. Object Storage Hosts Performance
Users typically access Object Storage through the proxy services, which run behind hardware load
balancers. By default, highly resilient storage system replicate stored data, which can affect the
overall system performance. In this case, 10 GbE or higher networking capacity is recommended
across the storage network architecture.
3.5.5. Controller Nodes
Controller nodes provide management services to the end-user and provide services internally for
the cloud operation. It is important to carefully design the hardware that is used to run the controller
infrastructure.
The controllers run message-queuing services for system messaging between the services.
Performance issues in messaging can lead to delays in operational functions such as spinning up
and deleting instances, provisioning new storage volumes, and managing network resources. These
delays can also adversely affect the ability of the application to react to some conditions, especially
when using auto-scaling features.
You also need to ensure that controller nodes can handle the workload of multiple concurrent users.
Ensure that the APIs and Horizon services are load-tested to improve service reliability for your
customers.
It is important to consider the OpenStack Identity Service (keystone), which provides authentication
and authorization for all services, internally to OpenStack and to end-users. This service can lead to
a degradation of overall performance if it is not sized appropriately.
Metrics that are critically important to monitor include:
Image disk utilization
Response time to the Compute API
3.6. MAINTENANCE AND SUPPORT
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To support and maintain an installation, OpenStack cloud management requires the operations staff
to understand the architecture design. The skill level and role separation between the operations and
engineering staff depends on the size and purpose of the installation.
Large cloud service providers, or telecom providers, are more likely to be managed by speciallytrained, dedicated operations organization.
Smaller implementations are more likely to rely on support staff that need to take on combined
engineering, design and operations functions.
If you incorporate features that reduce the operations overhead in the design, you might be able to
automate some operational functions.
Your design is also directly affected by terms of Service Level Agreements (SLAs). SLAs define
levels of service availability and usually include penalties if you do not meet contractual obligations.
SLA terms that affect the design include:
API availability guarantees that imply multiple infrastructure services and highly available load
balancers.
Network uptime guarantees that affect switch design and might require redundant switching and
power.
Network security policy requirements that imply network segregation or additional security
mechanisms.
3.6.1. Backups
Your design might be affected by your backup and restore strategy, data valuation or hierarchical
storage management, retention strategy, data placement, and workflow automation.
3.6.2. Downtime
An effective cloud architecture should support the following:
Planned downtime (maintenance)
Unplanned downtime (system faults)
For example, if a compute host fails, instances might be restored from a snapshot or by re-spawning
an instance. However, for high availability you might need to deploy additional support services such
as shared storage or design reliable migration paths.
3.7. AVAILABILITY
OpenStack can provide a highly-available deployment when you use at least two servers. The
servers can run all services from the RabbitMQ message queuing service and the MariaDB
database service.
When you scale services in the cloud, backend services also need to scale. Monitoring and reporting
server utilization and response times, as well as load testing your systems, can help determine
scaling decisions.
To avoid a single point of failure, OpenStack services should be deployed across multiple
servers. API availability can be achieved by placing these services behind highly-available load
balancers with multiple OpenStack servers as members.
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Ensure that your deployment has adequate backup capabilities. For example, in a deployment
with two infrastructure controller nodes using high availability, if you lose one controller you can
still run the cloud services from the other.
OpenStack infrastructure is integral to provide services and should always be available,
especially when operating with SLAs. Consider the number of switches, routes and redundancies
of power that are necessary for the core infrastructure, as well as the associated bonding of
networks to provide diverse routes to a highly available switch infrastructure. Pay special
attention to the type of networking backend to use. For information about how to choose a
networking backend, see Chapter 2, Networking In-Depth.
If you do not configure your Compute hosts for live-migration and a Compute host fails, the
Compute instance and any data stored on that instance might be lost. To do ensure the uptime
of your Compute hosts, you can use shared file systems on enterprise storage or OpenStack
Block storage.
External software can be used to check service availability or threshold limits and to set appropriate
alarms. The Operational Tools repository for Red Hat OpenStack Platform includes:
Sensu
Uchiwa dashboard
Note
For a reference architecture that uses high availability in OpenStack, see: Deploying
Highly Available Red Hat OpenStack Platform 6 with Ceph Storage
3.8. SECURITY
A security domain includes users, applications, servers, or networks that share common trust
requirements and expectations in a single system. Security domains typically use the same
authentication and authorization requirements and users.
Typical security domain categories are Public, Guest, Management, and Data. The domains can be
mapped to an OpenStack deployment individually or combined. For example, some deployment
topologies combine guest and data domains in one physical network, and in other cases the
networks are physically separated. Each case requires the cloud operator to be aware of the
relevant security concerns.
Security domains should be mapped to your specific OpenStack deployment topology. The domains
and the domain trust requirements depend on whether the cloud instance is public, private, or
hybrid.
Public domain
Entirely untrusted area of the cloud infrastructure. A public domain can refer to the Internet
as a whole or networks over which you have no authority. This domain should always be
considered untrusted.
Guest domain
Typically used for Compute instance-to-instance traffic and handles Compute data
generated by instances on the cloud, but not by services that support the operation of the
cloud, such as API calls.
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Public cloud providers and private cloud providers that do not have strict controls on
instance usage or that allow unrestricted Internet access to instances, should consider this
domain untrusted. Private cloud providers might consider this network internal and therefore
trusted only with controls that assert trust in instances and all cloud tenants.
Management domain
The domain where services interact with each other. This domain is sometimes known as
the control plane. Networks in this domain transfer confidential data, such as configuration
parameters, user names, and passwords. In most deployments, this domain is considered
trusted.
Data domain
The domain where storage services transfer data. Most data that crosses this domain has
high integrity and confidentiality requirements and, depending on the type of deployment,
might also have strong availability requirements. The trust level of this network is dependent
on other deployment decisions.
When you deploy OpenStack in an enterprise as a private cloud, the deployment is usually behind a
firewall and inside the trusted network with existing systems. Users of the cloud are typically
employees that are bound by the security requirements that the company defines. This deployment
implies that most of the security domains can be trusted.
However, when you deploy OpenStack in a public facing role, no assumptions can be made
regarding the trust level of the domains, and the attack vectors significantly increase. For example,
the API endpoints and the underlying software become vulnerable to bad actors that want to gain
unauthorized access or prevent access to services. These attacks might lead to loss data,
functionality, and reputation. These services must be protected using auditing and appropriate
filtering.
You must exercise caution also when you manage users of the system for both public and private
clouds. The Identity service can use external identity backends such as LDAP, which can ease user
management inside OpenStack. Because user authentication requests include sensitive information
such as user names, passwords, and authentication tokens, you should place the API services
behind hardware that performs SSL termination.
3.9. ADDITIONAL SOFTWARE
A typical OpenStack deployment includes OpenStack-specific components and Section 1.6.1,
“Third-party Components”. Supplemental software can include software for clustering, logging,
monitoring, and alerting. The deployment design must therefore account for additional resource
consumption, such as CPU, RAM, storage, and network bandwidth.
When you design your cloud, consider the following factors:
Databases and messaging
The underlying message queue provider might affect the required number of controller
services, as well as the technology to provide highly resilient database functionality. For
example, if you use MariaDB with Galera, the replication of services relies on quorum.
Therefore, the underlying database should consist of at least three nodes to account for the
recovery of a failed Galera node.
When you increase the number of nodes to support a software feature, consider both rack
space and switch port density.
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External caching
Memcached is a distributed memory object caching system, and Redis is a key-value store.
Both systems can be deployed in the cloud to reduce load on the Identity service. For
example, the memcached service caches tokens, and uses a distributed caching system to
help reduce some bottlenecks from the underlying authentication system.
Using memcached or Redis does not affect the overall design of your architecture, because
these services are typically deployed to the infrastructure nodes that provide the OpenStack
services.
Load balancing
Although many general-purpose deployments use hardware load balancers to provide
highly available API access and SSL termination, software solutions such as HAProxy can
also be considered. You must ensure that software-defined load balancing implementations
are also highly available.
You can configure software-defined high availability with solutions such as Keepalived or
Pacemaker with Corosync. Pacemaker and Corosync can provide active-active or activepassive highly available configuration based on the specific service in the OpenStack
environment.
These applications might affect the design because they require a deployment with at least
two nodes, where one of the controller nodes can run services in standby mode.
Logging and monitoring
Logs should be stored in a centralized location to make analytics easier. Log data analytics
engines can also provide automation and issue notification with mechanisms to alert and fix
common issues.
You can use external logging or monitoring software in addition to the basic OpenStack
logs, as long as the tools support existing software and hardware in your architectural
design. The Operational Tools repository for Red Hat OpenStack Platform includes the
following tools:
Fluentd
ElasticSearch
Kibana
3.10. PLANNING TOOL
The Cloud Resource Calculator tool can help you calculate capacity requirements.
To use the tool, enter your hardware details into the spreadsheet. The tool then shows a calculated
estimate of the number of instances available to you, including flavor variations.
Important
This tool is provided only for your convenience. It is not officially supported by Red Hat.
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CHAPTER 4. ARCHITECTURE EXAMPLES
This chapter contains references to architecture examples of Red Hat OpenStack Platform
deployments.
Note
All architecture examples in this guide assume that you deploy OpenStack Platform on
Red Hat Enterprise Linux 7.2 (or later) with the KVM hypervisor.
4.1. OVERVIEW
Typically, deployments are based on performance or functionality. Deployments can also be based
on deployed infrastructure.
Table 4.1. Deployments based on functionality or performance
Example
Description
Section 4.2, “GeneralPurpose Architecture”
General high-availability cloud to use if you are unsure of specific technical or
environmental needs. This architecture type is flexible, does not emphasize
any single OpenStack component, and is not restricted to specific
environments.
Section 4.3, “ComputeFocused Architecture”
Compute-focused cloud specifically that supports compute-intensive
workloads. Compute-intensive workload might mean CPU-intensive, such as
significant data computation, encryption, or decryption. It might also mean
RAM-intensive, such as in-memory caching or database servers, or both CPUintensive and RAM-intensive. It does not normally mean storage-intensive or
network-intensive. You can choose this architecture type if you require high
performance Compute resources.
Section 4.4.2, “Data
Analytics Architecture”
Performance-focused storage system designed for management and analysis
of large data sets, such as Hadoop clusters. In this architecture type,
OpenStack integrates with Hadoop to manage the Hadoop cluster with Ceph
as the storage backend.
Section 4.4.3, “HighPerformance Database
Architecture”
High-performance storage system that assumes increased database IO
requirements and utilizes a solid-state drive (SSD) to process data. You can
use this architecture type for existing storage environments.
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Example
Description
Section 4.5.2, “Cloud
Storage and Backup
Architecture”
Cloud-based file storage and sharing service, commonly used in OpenStack
deployments. This architecture type uses a cloud backup application, where
incoming data to the cloud traffic is higher than the outgoing data.
Section 4.5.3, “LargeScale Web-Application
Architecture”
Hardware-based load balancing cluster for a large-scale Web application. This
architecture type provides SSL-offload functionality and connects to tenant
networks to reduce address consumption and scale the Web application
horizontally.
4.2. GENERAL-PURPOSE ARCHITECTURE
You can deploy a general high availability cloud if you are unsure of specific technical or
environmental needs. This flexible architecture type does not emphasize any single OpenStack
component, and it is not restricted to particular environments.
This architecture type covers 80% of potential use cases, including:
Simple database
Web application runtime environment
Shared application development environment
Test environment
Environment requiring scale-out additions instead of than scale-up additions
This architecture type is not recommended for cloud domains that require increased security.
Note
For installation and deployment documentation, see Chapter 5, Deployment Information.
4.2.1. Example Use Case
An online classified advertising company wants to run web applications that include Apache Tomcat,
Nginx and MariaDB in a private cloud. To meet policy requirements, the cloud infrastructure will run
inside the company data center.
The company has predictable load requirements, but requires scaling to cope with nightly increases
in demand. The current environment does not have the flexibility to align with the company goal of
running an open-source API environment.
The current environment consists of the following components:
Between 120 and 140 installations of Nginx and Tomcat, each with 2 vCPUs and 4 GB of RAM
A three-node MariaDB and Galera cluster, each with 4 vCPUs and 8 GB RAM. Pacemaker is
used to manage the Galera nodes.
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The company runs hardware load balancers and multiple web applications that serve the websites.
Environment orchestration uses combinations of scripts and Puppet. The website generates large
amounts of log data every day that need to be archived.
4.2.2. About the Design
The architecture for this example includes three controller nodes and at least eight Compute nodes.
It uses OpenStack Object Storage for static objects and OpenStack Block Storage for all other
storage needs.
To ensure that the OpenStack infrastructure components are highly available, nodes use the
Pacemaker add-on for Red Hat Enterprise Linux together with HAProxy.
The architecture includes the following components:
Firewall, switches, and hardware load balancers for the public-facing network connections.
OpenStack controller service that run Image, Identity, and Networking, combined with the
support services MariaDB and RabbitMQ. These services are configured for high availability on
at least three controller nodes.
Cloud nodes are configured for high availability with the Pacemaker add-on for Red Hat
Enterprise Linux.
Compute nodes use OpenStack Block Storage for instances that need persistent storage.
OpenStack Object Storage to serve static objects, such as images.
4.2.3. Architecture Components
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Component
Description
Block Storage
Persistant storage for instances.
Compute controller
services
Compute management and scheduling services that run on the controller.
Dashboard
Web console for OpenStack management.
Identity
Basic authentication and authorization for users and tenants.
Image
Stores images to be used for booting instances and managing snapshots.
MariaDB
Database for all OpenStack components. MariaDB server instances store
data on shared enterprise storage, such as NetApp or Solidfire. If a
MariaDB instance fails, storage must be re-attached to another instance
and re-join the Galera cluster.
Networking
Controls hardware load balancers with plug-ins and the Networking API. If
you increase OpenStack Object Storage, you must consider network
bandwidth requirements. It is recommended to run OpenStack Object
Storage on network connections with 10 GbE or higher.
Object Storage
Processes and archives logs from the web application servers. You can
also use Object Storage to move static web content from OpenStack
Object Storage containers or to back up images that are managed by
OpenStack Image.
Telemetry
Monitoring and reporting for other OpenStack services.
4.2.4. Compute Node Requirements
The Compute service is installed on each of the Compute nodes.
This general-purpose architecture can run up to 140 web instances, and the small number of
MariaDB instances requires 292 vCPUs and 584 GB RAM. On a typical 1U server with dual-socket
hex-core Intel CPUs with Hyperthreading, and assuming 2:1 CPU over-commit ratio, this architecture
requires eight Compute nodes.
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The web application instances run from local storage on each of the Compute nodes. The web
application instances are stateless, so in case one of the instances fails, the application can
continue to run.
4.2.5. Storage Requirements
For storage, use a scaled-out solution with directly-attached storage in the servers. For example,
you can populate storage in the Compute hosts in a way that is similar to a grid-computing solution,
or in dedicated hosts that provide block storage exclusively.
If you deploy storage in the Compute hosts, ensure that the hardware can handle the storage and
compute services.
4.3. COMPUTE-FOCUSED ARCHITECTURE
Note
Cells are available in this release as a Technology Preview, and therefore are not fully
supported by Red Hat. They should only be used for testing, and should not be deployed
in a production environment. For more information about Technology Preview features,
see Scope of Coverage Details.
A compute-focused cloud supports CPU-intensive workloads such as data computation or
encryption and decryption, RAM-intensive workloads such as in-memory caching or database
servers, or both. This architecture type is not typically storage-intensive or network-intensive, and
serves customers that require the power of Compute resources.
Compute-focused workloads include the following use cases:
High-performance computing (HPC)
Big-data analytics using Hadoop or other distributed data stores
Continuous integration or continuous deployment (CI/CD)
Platform-as-a-Service (PaaS)
Signal processing for network function virtualization (NFV)
A compute-focused OpenStack cloud does not typically use raw block storage services because the
cloud does not generally host applications that require persistent block storage. Infrastructure
components are not shared, so that the workloads can consume as many available resources as
needed. Infrastructure components need to also be highly available. This design uses load
balancers. You can also use HAProxy.
Note
For installation and deployment documentation, see Chapter 5, Deployment Information.
4.3.1. Example Use Case
An organization provides HPC for research projects, and needs to add a third compute center to two
existing compute centers in Europe.
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The following table lists the requirements for each compute center to add:
Data Center
3.5 Mega Watts
91000 cores
120 PB HDD
100 PB Tape
Approximate capacity
Budapest, Hungary
Geneva,
Switzerland
2.5 Mega
Watts
20000 cores
6 PB HDD
310 TB Memory
4.3.2. About the Design
This architecture uses cells for segregation of compute resources and for transparent scaling
between different data centers. This decision impacts support for security groups and live migration.
In addition, it requires manual replication of some configuration elements, such as flavors, across
cells. However, cells provide the required scale while exposing a single public API endpoint to users.
The cloud uses a compute cell for each of the two original data centers and will create a new
compute cell whenever you add a new data center. Each cell contains three availability zones to
further segregate compute resources and at least three RabbitMQ message brokers configured for
clustering with mirrored queues for high availability.
The API cell, which resides behind an HAProxy load balancer, is in the data center in Switzerland.
The API cell directs API calls to compute cells using a customized variation of the cell scheduler.
The customizations allow certain workloads to route to a specific data center or to all data centers
based on cell RAM availability.
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You can also use filters to customize the Compute scheduler that handles placement in the cells.
For example, ImagePropertiesFilter provides special handling based on the operating system that
the guest runs, for example Linux or Windows.
A central database team manages the SQL database server in each cell in an active/passive
configuration with a NetApp storage backend. Backups run every six hours.
4.3.3. Architecture Components
Component
Description
Compute
Compute management and scheduling services run on the controller. The Compute
service also runs on each compute node.
Dashboard
Service
GUI for OpenStack management.
Identity
Service
Basic authentication and authorization functionality.
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Component
Description
Image Service
Runs in the API cell and maintains a small set of Linux images, onto which
orchestration tools can place applications.
Networking
Networking services. For more information about OpenStack Networking, see
Chapter 2, Networking In-Depth .
Monitoring
Telemetry service performs metering to adjust project quotas with a sharded, replicated
MongoDB backend. To spread the API load, you must deploy instances of the
openstack-nova-api service in the child cells that Telemetry can query. This
deployment also requires configuration of supporting services, such as Identity and
Image, in the child cells. Specific critical metrics to capture include Image disk
utilization and response time to the Compute API.
Orchestration
Service
Automatically deploys and tests new instances.
Telemetry
Service
Supports the Orchestration auto-scaling feature.
Object
Storage
Stores objects with a 3 PB Ceph cluster.
4.3.4. Design Considerations
In addition to basic design considerations described in Chapter 3, Design and compute node design
considerations described in Section 3.2, “Compute Resources”, the following items should be
considered for a compute-intensive architecture.
Workloads
Short-lived workloads can include continuous integration and continuous deployment (CICD) jobs, which create large numbers of compute instances simultaneously to perform a set
of compute-intensive tasks. The environment then copies the results or the artifacts from
each instance to long-term storage before it terminates the instances.
Long-lived workloads, such as a Hadoop cluster or an HPC cluster, typically receive large
data sets, perform the computational work on those data sets, and then push the results to
long-term storage. When the computational work ends, the instances are idle until they
receive another job. Environments for long-lived workloads are often larger and more
complex, but you can offset the cost of building these environments by keeping them active
between jobs.
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Workloads in a compute-focused OpenStack cloud generally do not require persistent block
storage, except some uses of Hadoop with HDFS. A shared file system or object store
maintains initial data sets and serves as the destination for saving the computational results.
By avoiding input-output (IO) overhead, you can significantly enhance workload
performance. Depending on the size of the data sets, you might need to scale the object
store or the shared file system.
Expansion Planning
Cloud users expect instant access to new resources as needed. Therefore, you must plan
for typical usage and for sudden spikes in resource demand. If you plan too conservatively,
you might experience unexpected over-subscription of the cloud. If you plan too
aggressively, you might experience unexpected underutilization of the cloud unnecessary
operations and maintenance costs.
The key factor in expansion planning is analytics of trends in cloud usage over time.
Measure the consistency with which you deliver services instead of the average speed or
capacity of the cloud. This information can help you model capacity performance and
determine the current and future capacity of the cloud.
For information about monitoring software, see Section 3.9, “Additional Software”.
CPU and RAM
Typical server offerings today include CPUs with up to 12 cores. In addition, some Intel
CPUs support Hyper-Threading Technology (HTT), which doubles the core capacity.
Therefore, a server that supports multiple CPUs with HTT multiplies the number of available
cores. HTT is an Intel proprietary simultaneous multi-threading implementation that is used
to improve parallelization on the Intel CPUs. Consider enabling HTT to improve the
performance of multi-threaded applications.
The decision to enable HTT on a CPU depends on the use case. For example, disabling
HTT can help intense computing environments. Running performance tests of local
workloads with and without HTT can help determine which option is more appropriate for a
particular case.
Capacity Planning
You can add capacity to your compute environment with one or more of the following
strategies:
Horizontally scale by adding extra capacity to the cloud. You should use the same, or
similar CPUs in the extra nodes to reduce the chance of breaking any live-migration
features. Scaling out hypervisor hosts also affects network and other data center
resources. Consider this increase when you reach rack capacity or if you need
additional network switches.
Note
Be aware of the additional work required to place the nodes in appropriate
availability zones and host aggregates.
Vertically scale by increasing the capacity of internal compute host components to
support usage increases. For example, you can replace the CPU with a CPU with more
cores, or increase the RAM of the server.
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Assess your average workload, and if need be, increase the number of instances that
can run in the compute environment by adjusting the over-commit ratio.
Important
Do not increase the CPU over-commit ratio in a compute-focused OpenStack
design architecture. Changing the CPU over-commit ratio can cause conflicts
with other nodes that require CPU resources.
Compute Hardware
A compute-focused OpenStack cloud is extremely demanding on processor and memory
resources. Therefore, you should prioritize server hardware that can offer more CPU
sockets, more CPU cores, and more RAM.
Network connectivity and storage capacity are less critical to this architecture. The hardware
must provide enough network connectivity and storage capacity to meet minimum user
requirements, but the storage and networking components primarily load data sets to the
computational cluster and do not require consistent performance.
Storage Hardware
You can build a storage array using commodity hardware with Open Source software, but
you might need specialized expertise to deploy it. You can also use a scale-out storage
solution with direct-attached storage in the servers, but you must ensure that the server
hardware supports the storage solution.
Consider the following factors when you design your storage hardware:
Availability. If instances must be highly available or capable of migration between hosts,
use a shared storage file system for ephemeral instance data to ensure that compute
services can run uninterrupted in the event of a node failure.
Latency. Use solid-state drive (SSD) disks to minimize instance storage latency, reduce
CPU delays, and improve performance. Consider using RAID controller cards in
compute hosts to improve the performance of the underlying disk sub-system.
Performance. Although a compute-focused cloud does not usually require major data I/O
to and from storage, storage performance is still an important factor to consider. You
can measure the storage hardware performance by observing the latency of storage I/O
requests. In some compute-intensive workloads, minimizing the delays that the CPU
experiences while fetching data from storage can significantly improve the overall
performance of the application.
Expandability. Decide the maximum capacity of the storage solution. For example, a
solution that expands to 50 PB is more expandable than a solution that only expands to
10PB. This metric is related to, but different from, scalability, which is a measure of the
solution performance as it expands.
Connectivity. The connectivity must satisfy the storage solution requirements. If you
select a centralized storage array, determine how to connect the hypervisors to the
storage array. Connectivity can affect latency and performance. Therefore, ensure that
the network characteristics minimize latency to boost the overall performance of the
environment.
Network Hardware
In addition to basic network considerations described in Chapter 2, Networking In-Depth,
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consider the following factors:
The required port count affects the physical space that a network design requires. For
example, a switch that provides 48 ports with 10 GbE capacity for each port in a 1U
server has higher port density than a switch that provides 24 ports with 10 GbE capacity
for each port in a 2U server. Higher port density allows more rack space for compute or
storage components. You must also consider fault domains and power density.
Although more expensive, you can also consider higher density switches as you should
not design the network beyond functional requirements.
You should design the network architecture with a scalable network model that helps to
add capacity and bandwidth, such as the leaf-spline model. In this type of network
design, you can add additional bandwidth as well as scale out to additional racks of
gear.
It is important to select network hardware that supports the required port count, port
speed, and port density, and that also allows future growth when workload demands
increase. It is also important to evaluate where in the network architecture it is valuable
to provide redundancy. Increased network availability and redundancy can be
expensive, so you should compare the extra cost with the benefits of redundant network
switches and bonded interfaces at the host level.
4.4. STORAGE-FOCUSED ARCHITECTURES
Section 4.4.1, “Storage-Focused Architecture Types”
Section 4.4.2, “Data Analytics Architecture”
Section 4.4.3, “High-Performance Database Architecture”
Section 4.4.4, “Storage-Focused Architecture Considerations”
4.4.1. Storage-Focused Architecture Types
The cloud storage model stores data in logical pools on physical storage devices. This architecture
is often referred to as an integrated storage cloud.
Cloud storage commonly refers to a hosted object storage service. However, the term can also
include other types of data storage that are available as a service. OpenStack offers both Block
Storage (cinder) and Object Storage (swift). Cloud storage typically runs on a virtual infrastructure
and resembles broader cloud computing in interface accessibility, elasticity, scalability, multitenancy, and metered resources.
You can use cloud storage services on-premise or off-premises. Cloud storage is highly fault
tolerant with redundancy and data distribution, is highly durable with versioned copies, and can
perform consistent data replication.
Example cloud storage applications include:
Active archive, backups and hierarchical storage management
General content storage and synchronization such as a private DropBox service
Data analytics with parallel file systems
Unstructured data store for services such as social media backend storage
Persistent block storage
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Operating system and application image store
Media streaming
Databases
Content distribution
Cloud storage peering
For more information about OpenStack storage services, see Section 1.2.2, “OpenStack Object
Storage (swift)” and Section 1.2.1, “OpenStack Block Storage (cinder)”.
4.4.2. Data Analytics Architecture
Analysis of large data sets is highly dependent on the performance of the storage system. Parallel
file systems can provide high-performance data processing and are recommended for large scale
performance-focused systems.
Note
For installation and deployment documentation, see Chapter 5, Deployment Information.
4.4.2.1. About the Design
OpenStack Data Processing (sahara) integrates with Hadoop to manage the Hadoop cluster inside
the cloud. The following diagram shows an OpenStack store with a high-performance requirement.
The hardware requirements and configuration are similar to the high-performance architecture
described in Section 4.4.3, “High-Performance Database Architecture”. In this example, the
architecture uses the Ceph Swift-compatible REST interface that connects to a caching pool and
enables acceleration of the available pool.
4.4.2.2. Architecture Components
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Component
Description
Compute
Compute management and scheduling services run on the controller. The Compute
service also runs on each compute node.
Dashboard
Web console for OpenStack management.
Identity
Basic authentication and authorization functionality.
Image
Stores images to be used for booting instances and managing snapshots. This service
runs on the controller and offers a small set of images.
Networking
Networking services. For more information about OpenStack Networking, see
Chapter 2, Networking In-Depth .
Telemetry
Monitoring and reporting for other OpenStack services. Use this service to monitor
instance usage and adjust project quotas.
Object
Storage
Stores data with the Hadoop backend.
Block Storage
Stores volumes with the Hadoop backend.
Orchestration
Manages templates for instances and block storage volume. Use this service to launch
additional instances for storage-intensive processing, with Telemetry for auto-scaling.
4.4.2.3. Cloud Requirements
Requirement
Description
Performance
To boost performance, you can choose specialty solutions to cache disk activity.
Security
You must protect data both in transit and at rest.
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Requirement
Description
Storage
proximity
In order to provide high performance or large amounts of storage space, you might
need to attach the storage to each hypervisor or serve it from a central storage device.
4.4.2.4. Design Considerations
In addition to basic design considerations described in Chapter 3, Design, you should also follow the
considerations described in Section 4.4.4, “Storage-Focused Architecture Considerations”.
4.4.3. High-Performance Database Architecture
Database architectures benefit from high performance storage backends. Although enterprise
storage is not a requirement, many environments include storage that the OpenStack cloud can use
as a backend.
You can create a storage pool to provide block devices with OpenStack Block Storage for instances
and object interfaces. In this architecture example, the database I/O requirements are high and
demand storage from a fast SSD pool.
4.4.3.1. About the Design
The storage system uses a LUN backed with a set of SSDs in a traditional storage array, and uses
OpenStack Block Storage integration or a storage platform such as Ceph.
This system can provide additional performance capabilities. In the database example, a portion of
the SSD pool can act as a block device to the database server. In the high performance analytics
example, the inline SSD cache layer accelerates the REST interface.
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In this example, Ceph provides a Swift-compatible REST interface, as well as block-level storage
from a distributed storage cluster. It is highly flexible and enables reduced cost of operations with
features such as self-healing and auto-balancing. Erasure coded pools are recommended to
maximize the amount of usable space.
Note
Erasure coded pools require special considerations, such as higher computational
requirements and limitations on which operations are allowed on an object. Erasure coded
pools do not support partial writes.
4.4.3.2. Architecture Components
Component
Description
Compute
Compute management and scheduling services run on the controller. The Compute
service also runs on each compute node.
Dashboard
Web console for OpenStack management.
Identity
Basic authentication and authorization functionality.
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Component
Description
Image
Stores images to be used for booting instances and managing snapshots. This service
runs on the controller and offers a small set of images.
Networking
Networking services. For more information about OpenStack Networking, see
Chapter 2, Networking In-Depth .
Telemetry
Monitoring and reporting for other OpenStack services. Use this service to monitor
instance usage and adjust project quotas.
Monitoring
Use the Telemetry service to perform metering for the purposes of adjusting project
quotas.
Object
Storage
Stores data with the Ceph backend.
Block Storage
Stores volumes with the Ceph backend.
Orchestration
Manages templates for instances and block storage volume. Use this service to launch
additional instances for storage-intensive processing, with Telemetry for auto-scaling.
4.4.3.3. Hardware Requirements
You can use an SSD cache layer to link block devices directly to hypervisors or to instances. The
REST interface can also use the SSD cache systems as an inline cache.
Componen
t
74
Requirement
Network
CHAPTER 4. ARCHITECTURE EXAMPLES
Componen
t
Requirement
Network
10 GbE
horizontally
scalable
spine-leaf
backend
storage and
front-end
network
Storage hardware
* 5 storage
servers for
caching
layer 24x1
TB SSD
* 10
storage
servers
with 12x4
TB disks
for each
server,
which
equals 480
TB total
space with
approximat
ely 160 TB
of usable
space after
3 replicas
4.4.3.4. Design Considerations
In addition to basic design considerations described in Chapter 3, Design, you should also follow the
considerations described in Section 4.4.4, “Storage-Focused Architecture Considerations”.
4.4.4. Storage-Focused Architecture Considerations
In addition to basic design considerations described in Chapter 3, Design and to storage node
design described in Section 3.3, “Storage Resources”, the following items should be considered for
a storage-intensive architecture.
Connectivity
Ensure the connectivity matches the storage solution requirements. If you select a
centralized storage array, determine how to connect the hypervisors to the array.
Connectivity can affect latency and performance. Confirm that the network characteristics
minimize latency to boost the overall performance of the design.
Density
Instance density. In a storage-focused architecture, instance density and CPU/RAM
over-subscription are lower. You need more hosts to support the anticipated scale,
especially if the design uses dual-socket hardware designs.
Host density. You can address the higher host count with a quad-socket platform. This
platform decreases host density and increases rack count. This configuration affects the
number of power connections and also impacts network and cooling requirements.
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Red Hat OpenStack Platform 9 Architecture Guide
Power and cooling. The power and cooling density requirements might be lower with 2U,
3U, or 4U servers than with blade, sled, or 1U server designs. This configuration is
recommended for data centers with older infrastructure.
Flexibility
Organizations need to have the flexibility to choose between off-premise and on-premise
cloud storage options. For example, continuity of operations, disaster recovery, security,
and records retention laws, regulations, and policies can impact the cost-effectiveness of
the storage provider.
Latency
Solid-state drives (SSDs) can minimize latency for instance storage and reduce CPU delays
that storage latency might cause. Evaluate the gains from using RAID controller cards in
compute hosts to improve the performance of the underlying disk sub-system.
Monitors and alerts
Monitoring and alerting services are critical in cloud environments with high demands on
storage resources. These services provide a real-time view into the health and performance
of the storage systems. An integrated management console, or other dashboards that
visualize SNMP data, helps to discover and resolve issues with the storage cluster.
A storage-focused cloud design should include:
Monitoring of physical hardware and environmental resources, such as temperature and
humidity.
Monitoring of storage resources, such as available storage, memory, and CPU.
Monitoring of advanced storage performance data to ensure that storage systems are
performing as expected.
Monitoring of network resources for service disruptions which affect access to storage.
Centralized log collection and log-analytics capabilities.
Ticketing system, or integration with a ticketing system, to track issues.
Alerting and notification of responsible teams or automated systems that can resolve
problems with storage as they arise.
Network Operations Center (NOC) staffed and always available to resolve issues.
Scaling
A storage-focused OpenStack architecture should focus on scaling up instead of scaling out.
You should determine whether a smaller number of larger hosts or a larger number of
smaller hosts based on factors such as cost, power, cooling, physical rack and floor space,
support-warranty, and manageability.
4.5. NETWORK-FOCUSED ARCHITECTURES
Section 4.5.1, “Network-Focused Architecture Types”
Section 4.5.2, “Cloud Storage and Backup Architecture”
Section 4.5.3, “Large-Scale Web-Application Architecture”
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CHAPTER 4. ARCHITECTURE EXAMPLES
Section 4.5.4, “Network-Focused Architecture Considerations”
4.5.1. Network-Focused Architecture Types
All OpenStack deployments depend on network communication to function properly because of their
service-based nature. However, in some cases the network configuration is more critical and
requires additional design considerations.
The following table describes common network-focused architectures. These architectures depend
on a reliable network infrastructure and on services that satisfy user and application requirements.
Architecture
Description
Big data
Clouds used for the management and collection of big data create significant demand
on network resources. Big data often uses partial replicas of the data to maintain
integrity over large distributed clouds. Big data applications that require a large amount
of network resources include Hadoop, Cassandra, NuoDB, Riak, or other NoSQL and
distributed databases.
Content
delivery
network (CDN)
CDNs can be used to stream video, view photographs, host web conferences, or
access any distributed cloud-based data repository by a large number of end-users.
Network configuration affects latency, bandwidth, and distribution of instances. Other
factors that affect content deliver and performance include network throughput of
backend systems, resource locations, WAN architecture, and cache methodology.
High
availability
(HA)
HA environments are dependent on network sizing that maintains replication of data
between sites. If one site becomes unavailable, additional sites can serve the increased
load until the original site returns to service. It is important to size network capacity to
handle the additional loads.
High
performance
computing
(HPC)
HPC environments require additional consideration of traffic flows and usage patterns
to address the needs of cloud clusters. HPC has high east-west traffic patterns for
distributed computing within the network, but can also have substantial north-south
traffic in and out of the network, depending on the application.
High-speed or
high-volume
transactional
systems
These application types are sensitive to network jitter and latency. Example
environments include financial systems, credit card transaction applications, and trading
systems. These architectures must balance a high volume of east-west traffic with
north-south traffic to maximize data delivery efficiency. Many of these systems must
access large, high-performance database backends.
Network
management
functions
Environments that support delivery of backend network services such as DNS, NTP, or
SNMP. You can use these services for internal network management.
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Red Hat OpenStack Platform 9 Architecture Guide
Architecture
Description
Network
service
offerings
Environments that run customer-facing network tools to support services. Examples
include VPNs, MPLS private networks, and GRE tunnels.
Virtual desktop
infrastructure
(VDI)
VDI systems are sensitive to network congestion, latency, and jitter. VDI requires
upstream and downstream traffic, and cannot rely on caching to deliver the application
to the end-user.
Voice over IP
(VoIP)
VoIP systems are sensitive to network congestion, latency, and jitter. VoIP system have
symmetrical traffic patterns and require network quality of service (QoS) for best
performance. In addition, you can implement active queue management to deliver voice
and multimedia content. Users are sensitive to latency and jitter fluctuations and can
detect them at very low levels.
Video or web
conference
Conferencing systems are sensitive to network congestion, latency, and jitter. Video
conferencing systems have symmetrical traffic pattern, but if the network is not hosted
on an MPLS private network, the system cannot use network quality of service (QoS) to
improve performance. Similar to VoIP, users of these system notice network
performance problems even at low levels.
Web portals or
services
Web servers are common applications in cloud services, and require an understanding
of the network requirements. The network must scale out to meet user demand and to
deliver web pages with minimum latency. Depending on the details of the portal
architecture, you should consider the internal east-west and north-south network
bandwidth when you plan the architecture.
4.5.2. Cloud Storage and Backup Architecture
This architecture is for a cloud that provides file storage and file-sharing. You might consider this a
storage-focused use case, but the network-side requirements make it a network-focused use case.
Note
For installation and deployment documentation, see Chapter 5, Deployment Information.
4.5.2.1. About the Design
The following cloud-backup application workload has two specific behaviors that impact the network.
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CHAPTER 4. ARCHITECTURE EXAMPLES
Because this workload includes an externally-facing service and an internally-replicating application,
it requires north-south and east-west traffic considerations.
North-south traffic
North-south traffic consists of data that moves in and out of the cloud. When a user uploads
and stores content, that content moves southbound into the OpenStack environment. When
users download content, that content moves northbound out of the OpenStack environment.
Because this service operates primarily as a backup service, most of the traffic moves
southbound into the environment. In this situation, you should configure a network to be
asymmetrically downstream, because the traffic that enters the OpenStack environment is
greater than the traffic that leaves the environment.
East-west traffic
East-west traffic consists of data that moves inside the environment. This traffic is likely to
be fully symmetric, because replication originates from any node and might target multiple
nodes algorithmically. However, this traffic might interfere with north-south traffic.
4.5.2.2. Architecture Components
Component
Description
Compute
Compute management and scheduling services run on the controller. The Compute
service also runs on each compute node.
Dashboard
Web console for OpenStack management.
Identity
Basic authentication and authorization functionality.
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Red Hat OpenStack Platform 9 Architecture Guide
Component
Description
Image
Stores images to be used for booting instances and managing snapshots. This service
runs on the controller and offers a small set of images.
Networking
Networking services. For more information about OpenStack Networking, see
Chapter 2, Networking In-Depth .
Object
Storage
Stores backup content.
Telemetry
Monitoring and reporting for other OpenStack services.
4.5.2.3. Design Considerations
In addition to basic design considerations described in Chapter 3, Design, you should also follow the
considerations described in Section 4.5.4, “Network-Focused Architecture Considerations”.
4.5.3. Large-Scale Web-Application Architecture
This architecture is for a large-scale web application that scales horizontally in bursts and generates
a high instance count. The application requires an SSL connection to secure data and must not lose
connection to individual servers.
Note
For installation and deployment documentation, see Chapter 5, Deployment Information.
4.5.3.1. About the Design
The following diagram shows an example design for this workload.
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CHAPTER 4. ARCHITECTURE EXAMPLES
This design includes the following components and workflows:
Hardware load balancer provides SSL offload functionality and connects to tenant networks to
reduce address consumption.
The load balancer links to the routing architecture while it services the virtual IP (VIP) for the
application.
The router and the load balancer use the GRE tunnel ID of the application tenant network, and
an IP address that is located in the tenant subnet but outside of the address pool. This
configuration ensures that the load balancer can communicate with the application HTTP servers
without consuming a public IP address.
4.5.3.2. Architecture Components
A web service architecture can consist of many options and optional components. Therefore, this
architecture can be used in multiple OpenStack designs. However, some key components must be
deployed to handle most web-scale workloads.
Component
Description
Compute
Compute management and scheduling services run on the controller. The Compute
service also runs on each compute node.
Dashboard
Web console for OpenStack management.
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Red Hat OpenStack Platform 9 Architecture Guide
Component
Description
Identity
Basic authentication and authorization functionality.
Image
Stores images to be used for booting instances and managing snapshots. This service
runs on the controller and offers a small set of images.
Networking
Networking services. A split network configuration is compatible with databases that
reside on private tenant networks, because the databases do not emit a large quantity
of broadcast traffic and might need to interconnect to other databases for content.
Orchestration
Manages instance templates to use when scaling out and during traffic bursts.
Telemetry
Monitoring and reporting for other OpenStack services. Use this service to monitor
instance usage and invoke instance templates from the Orchestration service.
Object
Storage
Stores backup content.
4.5.3.3. Design Considerations
In addition to basic design considerations described in Chapter 3, Design, you should also follow the
considerations described in Section 4.5.4, “Network-Focused Architecture Considerations”.
4.5.4. Network-Focused Architecture Considerations
In addition to basic design considerations described in Chapter 3, Design and to network node
design described in Chapter 2, Networking In-Depth, the following items should be considered for a
network-intensive architecture.
External dependencies
Consider using the following external network components:
Hardware load balancers to distribute workloads or off-load certain functions
External devices to implement dynamic routing
Although OpenStack Networking provides a tunneling feature, it is restricted to networkingmanaged regions. To extend a tunnel beyond the OpenStack regions to another region or to
an external system, implement the tunnel outside OpenStack or use a tunnel-management
system to map the tunnel or the overlay to an external tunnel.
Maximum transmission unit (MTU)
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CHAPTER 4. ARCHITECTURE EXAMPLES
Some workloads require a larger MTU due to the transfer of large blocks of data. When
providing network service for applications such as video streaming or storage replication,
configure the OpenStack hardware nodes and the supporting network equipment for jumbo
frames wherever possible. This configuration maximizes available bandwidth usage.
Configure jumbo frames across the entire path that the packets traverse. If one network
component cannot handle jumbo frames, the entire path reverts to the default MTU.
NAT usage
If you need floating IPs instead of fixed public IPs, you must use NAT. For example, use a
DHCP relay mapped to the DHCP server IP. In this case, it is easier to automate the
infrastructure to apply the target IP to a new instance, instead of reconfiguring legacy or
external systems for each new instance.
The NAT for floating IPs that is managed by OpenStack Networking resides in the
hypervisor. However, other versions of NAT might be running elsewhere. If there is a
shortage of IPv4 addresses, you can use the following methods to mitigate the shortage
outside of OpenStack:
Run a load balancer in OpenStack as an instance or externally as a service. The
OpenStack Load-Balancer-as-a-Service (LBaaS) can manage load balancing software
such as HAproxy internally. This service manages the Virtual IP (VIP) addresses while a
dual-homed connection from the HAproxy instance connects the public network with the
tenant private network that hosts all content servers.
Use a load balancer to serve the VIP and also connect to the tenant overlay network
with external methods or private addresses.
In some cases it may be desirable to use only IPv6 addresses on instances and operate
either an instance or an external service to provide a NAT-based transition technology such
as NAT64 and DNS64. This configuration provides a globally-routable IPv6 address, while
consuming IPv4 addresses only as necessary.
Quality of Service (QoS)
QoS impacts network-intensive workloads because it provides instant service to packets
with high priority because of poor network performance. In applications such as Voice over
IP (VoIP), differentiated service code points are usually required for continued operation.
You can also use QoS for mixed workloads to prevent low-priority, high-bandwidth
applications such as backup services, video conferencing, or file sharing, from blocking
bandwidth that is needed for the continued operation of other workloads.
You can tag file-storage traffic as lower class traffic, such as best effort or scavenger, to
allow higher-priority traffic to move through the network. In cases where regions in a cloud
are geographically distributed, you might also use WAN optimization to reduce latency or
packet loss.
Workloads
The routing and switching architecture should accommodate workdloads that require
network-level redundancy. The configuration depends on your selected network hardware,
on the selected hardware performance, and on your networking model. Examples include
Link Aggregation (LAG) and Hot Standby Router Protocol (HSRP).
The workload also impacta the effectiveness of overlay networks. If application network
connections are small, short lived, or bursty, running a dynamic overlay can generate as
much bandwidth as the packets the network carries. Overlay can also induce enough
latency to cause issues with the hypervisor, which causes performance degradation on
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Red Hat OpenStack Platform 9 Architecture Guide
packet-per-second and connection-per-second rates.
By default, overlays include a secondary full-mesh option that depends on the workload. For
example, most web services applications do not have major issues with a full-mesh overlay
network, and some network monitoring tools or storage replication workloads have
performance issues with throughput or excessive broadcast traffic.
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CHAPTER 5. DEPLOYMENT INFORMATION
CHAPTER 5. DEPLOYMENT INFORMATION
The following table includes deployment references for components mentioned in this guide.
Additional manuals for Red Hat OpenStack Platform can be found here:
https://access.redhat.com/documentation/en-US/Red_Hat_OpenStack_Platform/
Component
Reference
Red Hat Enterprise Linux
Red Hat OpenStack Platform is supported on Red Hat Enterprise Linux 7.2
or later. For information on installing Red Hat Enterprise Linux, see the
corresponding installation guide at:
https://access.redhat.com/site/documentation/enUS/Red_Hat_Enterprise_Linux/.
OpenStack
To install OpenStack components and their dependencies, use the Red Hat
OpenStack Platform director. The director uses a basic OpenStack
undercloud, which is then used to provision and manage the OpenStack
nodes in the final overcloud.
Be aware that you will need one extra host machine for the installation of
the undercloud, in addition to the environment necessary for the deployed
overcloud. For detailed instructions, see Red Hat OpenStack Platform
Director Installation and Usage.
High Availability
For the configuration of additional high availability components (for
example, HAProxy), see Deploying Highly Available Red Hat OpenStack
Platform 6 with Ceph Storage.
For information about configuring live migration, see Managing Instances
and Images.
LBasS
To use Load Balancing-as-a-Service, see "Configuring Load Balancing-asa-Service" in the Networking Guide.
Pacemaker
Pacemaker is integrated into Red Hat Enterprise Linux as an add-on. To
set up Red Hat Enterprise Linux for high availability, see the High
Availability Add-on Overview.
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