High Availability in Cloud Foundry
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This topic describes the components used to ensure high availability in Cloud Foundry Application Runtime, vertical and horizontal scaling, and the infrastructure required to support scaling component VMs for high availability.
A system with high availability provides higher than typical uptime through redundancy of apps and component VMs. You can create the redundancy required for high availability in several ways, such as running VMs in multiple availability zones and using external blob storage solutions.
The sections in this topic provide guidance on configuring your CFAR deployment for high availability.
This section describes how you can use availability zones, external load balancers, and external blob storage to ensure high availability for your deployment.
Availability Zones (AZs) are locations where public cloud services offer data centers.
You can assign and scale components in multiple AZs to help maintain high availability through redundancy. To configure sufficient redundancy, deploy CFAR across three or more AZs and assign multiple component instances to different AZs.
Always use an odd number of AZs. This ensures that your deployment remains available as long as greater than half of the AZs are available.
For example, a deployment with three AZs stays available when one AZ is unavailable. A deployment with five AZs stays available when two AZs are unavailable.
External load balancers distribute traffic coming from the internet to your internal network.
To ensure high availability for production environments, use a highly-available customer-provided external load balancing solution that does the following:
- Provides load balancing to each of the CFAR Router IP addresses
- Supports SSL termination with wildcard DNS location
- Adds appropriate x-forwarded-for and x-forwarded-proto HTTP headers to incoming requests
- (Optional) Supports WebSockets
For lab and test environments, the
use-haproxy.yml ops file enables HAProxy for your foundation.
Blobs are large binary files, such as PDFs or images. To store blobs for high availability, use external storage such as Amazon S3 or an S3-compatible service.
You can also store blobs internally using WebDAV or NFS. These components run as single instances and you cannot scale them. For these deployments, use the high availability features of your IaaS to immediately recover your WebDAV or NFS server VM if it fails.
The singleton Collector and Compilation components do not affect platform availability.
You can scale platform capacity in the following ways:
Vertical scaling: Add memory and disk to each VM.
Horizontal scaling: Add more VMs that run instances of CFAR components.
The type of apps you host on CFAR determines whether you scale vertically or horizontally.
For more information about scaling applications and maintaining app uptime, see the following topics:
Scaling vertically means adding memory and disk to your component VMs.
To scale vertically, allocate and maintain the following:
- Free space on host Diego cell VMs so that apps expected to deploy can successfully be staged and run.
- Disk space and memory in your deployment so that if one host VM is down, all instances of apps can be placed on the remaining host VMs.
- Free space to handle one AZ going down if deploying in multiple AZs.
Scaling horizontally means increasing the number of instances of VMs that run a functional component of a system.
You can horizontally scale most CFAR component VMs to multiple instances for high availability.
You should also distribute the instances of components across different AZs to minimize downtime during ongoing operation, product updates, and platform upgrades. For more information about using AZs, see Availability Zones.
The following table provides recommended instance counts for a high-availability deployment. You can decrease the footprint of your deployment by specifying fewer instances and combining multiple components onto a single VM.
|Diego Cell||≥ 2||The optimal balance between CPU/memory sizing and instance count depends on the performance characteristics of the apps that run on Diego cells. Scaling vertically with larger Diego cells makes for larger points of failure, and more apps go down when a cell fails. On the other hand, scaling horizontally decreases the speed at which the system rebalances apps. Rebalancing 100 cells takes longer and demands more processing overhead than rebalancing 20 cells.|
|Diego Brain||≥ 2||One per AZ, or two if only one AZ.|
|Diego BBS||≥ 2||One per AZ, or two if only one AZ.|
|PostgreSQL Server||0 or 1||
|MySQL Proxy||≥ 2|
|NATS Server||≥ 2||You might run a single NATS instance if you lack the resources to deploy two stable NATS servers. Components using NATS are resilient to message failures and the BOSH resurrector recovers the NATS VM quickly if it becomes non-responsive.|
|Cloud Controller API||≥ 2||Scale the Cloud Controller to accommodate the number of requests to the API and the number of apps in the system.|
|Cloud Controller Worker||≥ 2||Scale the Cloud Controller to accommodate the number of asynchronous requests to the API and background jobs.|
|Router||≥ 2||Scale the router to accommodate the number of incoming requests. Additional instances increase available bandwidth. In general, this load is much less than the load on host VMs.|
|Doppler Server||≥ 2||Deploying additional Doppler servers splits traffic across them. For high availability, use at least two per Availability Zone.|
|Loggregator TC||≥ 2||Deploying additional Loggregator Traffic Controllers allows you to direct traffic to them in a round-robin manner. For high availability, use at least two per Availability Zone.|
|etcd||≥ 3||Set this to an odd number equal to or one greater than the number of AZs you have, in order to maintain quorum. Distribute the instances evenly across the AZs, at least one instance per AZ.|
The ability to scale component VMs is important for high availability. To scale component VMs, you must ensure that the surrounding infrastructure of your deployment supports VM scaling.
This section describes the infrastructure required to support scaling component VMs for high availability.
Setting `max_in_flight` values
For each component, the variable
max_in_flight limits how many instances of that component are restarted simultaneously during updates or upgrades. You set
max_in_flight in the manifest as a system-wide value, plus any component-specific overrides. Values for
max_in_flight can be any integer between 1 and 100.
To ensure zero downtime during updates, set
max_in_flight for each component to a number low enough to prevent overburdening the component instances left running. Here are some guidelines:
- For host VMs, the closer their resource usage is to 100%, the lower you should set
max_in_flight, to allow non-migrating cells to pick up the work of cells stopping and restarting for migration. If resource usage is already close to 100%, scale up your host VMs before any updates.
- For quorum-based components like etcd and Diego BBS, set
- For other components, set
max_in_flightto the number of instances that you can afford to have down at any one time. This depends on your capacity planning. With higher redundancy, you can make the number high so that updates run faster. But if your components are at high utilization, you should keep the number low.
max_in_flight to a value greater than or equal to the number of instances you have running for a component.
Each IaaS has different ways of limiting resource consumption for scaling VMs. Consult with your IaaS administrator to ensure additional VMs and related resources, like IPs and storage, are available to scale.
For more information about configuring your resource pools according to the requirements of your deployment, see Building a Manifest in the BOSH documentation.
For database services deployed outside CFAR, use the high availability features included with your infrastructure. Also, configure backup and restore where possible.
Note: Data services may have single points of failure depending on their configuration.Create a pull request or raise an issue on the source for this page in GitHub