OpsGuide/Capacity planning and scaling

Cloud-based applications typically request more discrete hardware (horizontal scaling) as opposed to traditional applications, which require larger hardware to scale (vertical scaling).

OpenStack is designed to be horizontally scalable. Rather than switching to larger servers, you procure more servers and simply install identically configured services. Ideally, you scale out and load balance among groups of functionally identical services (for example, compute nodes or  nodes), that communicate on a message bus.

Determining cloud scalability
Determining the scalability of your cloud and how to improve it requires balancing many variables. No one solution meets everyone’s scalability goals. However, it is helpful to track a number of metrics. You can define virtual hardware templates called “flavors” in OpenStack, which will impact your cloud scaling decisions. These templates define sizes for memory in RAM, root disk size, amount of ephemeral data disk space available, and the number of CPU cores.

The default OpenStack flavors are shown in Table. OpenStack default flavors.

The starting point is the core count of your cloud. By applying some ratios, you can gather information about:


 * The number of virtual machines (VMs) you expect to run,
 * How much storage is required

You can use these ratios to determine how much additional infrastructure you need to support your cloud.

Here is an example using the ratios for gathering scalability information for the number of VMs expected as well as the storage needed. The following numbers support (200 / 2) × 16 = 1600 VM instances and require 80 TB of storage for :


 * 200 physical cores.
 * Most instances are size m1.medium (two virtual cores, 50 GB of storage).
 * Default CPU overcommit ratio ( in the   file) of 16:1.

However, you need more than the core count alone to estimate the load that the API services, database servers, and queue servers are likely to encounter. You must also consider the usage patterns of your cloud.

As a specific example, compare a cloud that supports a managed web-hosting platform with one running integration tests for a development project that creates one VM per code commit. In the former, the heavy work of creating a VM happens only every few months, whereas the latter puts constant heavy load on the cloud controller. You must consider your average VM lifetime, as a larger number generally means less load on the cloud controller.

Aside from the creation and termination of VMs, you must consider the impact of users accessing the service particularly on  and its associated database. Listing instances garners a great deal of information and, given the frequency with which users run this operation, a cloud with a large number of users can increase the load significantly. This can occur even without their knowledge. For example, leaving the OpenStack dashboard instances tab open in the browser refreshes the list of VMs every 30 seconds.

After you consider these factors, you can determine how many cloud controller cores you require. A typical eight core, 8 GB of RAM server is sufficient for up to a rack of compute nodes — given the above caveats.

You must also consider key hardware specifications for the performance of user VMs, as well as budget and performance needs, including storage performance (spindles/core), memory availability (RAM/core), network bandwidth hardware specifications and (Gbps/core), and overall CPU performance (CPU/core).

Adding cloud controller nodes
You can facilitate the horizontal expansion of your cloud by adding nodes. Adding compute nodes is straightforward since they are easily picked up by the existing installation. However, you must consider some important points when you design your cluster to be highly available.

A cloud controller node runs several different services. You can install services that communicate only using the message queue internally—  and   on a new server for expansion. However, other integral parts require more care.

You should load balance user-facing services such as dashboard,, or the Object Storage proxy. Use any standard HTTP load-balancing method (DNS round robin, hardware load balancer, or software such as Pound or HAProxy). One caveat with dashboard is the VNC proxy, which uses the WebSocket protocol— something that an L7 load balancer might struggle with. See also Horizon session storage.

You can configure some services, such as  and , to use multiple processes by changing a flag in their configuration file allowing them to share work between multiple cores on the one machine.

Segregating your cloud
Segregating your cloud is needed when users require different regions for legal considerations for data storage, redundancy across earthquake fault lines, or for low-latency API calls. It can be segregated by cells, regions, availability zones, or host aggregates.

Each method provides different functionality and can be best divided into two groups:


 * Cells and regions, which segregate an entire cloud and result in running separate Compute deployments.
 * Availability zones and host aggregates, which merely divide a single Compute deployment.

Table. OpenStack segregation methods provides a comparison view of each segregation method currently provided by OpenStack Compute.

Cells and regions
OpenStack Compute cells are designed to allow running the cloud in a distributed fashion without having to use more complicated technologies, or be invasive to existing nova installations. Hosts in a cloud are partitioned into groups called cells. Cells are configured in a tree. The top-level cell (“API cell”) has a host that runs the  service, but no   services. Each child cell runs all of the other typical  services found in a regular installation, except for the   service. Each cell has its own message queue and database service and also runs, which manages the communication between the API cell and child cells.

This allows for a single API server being used to control access to multiple cloud installations. Introducing a second level of scheduling (the cell selection), in addition to the regular  selection of hosts, provides greater flexibility to control where virtual machines are run.

Unlike having a single API endpoint, regions have a separate API endpoint per installation, allowing for a more discrete separation. Users wanting to run instances across sites have to explicitly select a region. However, the additional complexity of a running a new service is not required.

The OpenStack dashboard (horizon) can be configured to use multiple regions. This can be configured through the  parameter.

Availability zones and host aggregates
You can use availability zones, host aggregates, or both to partition a nova deployment. Both methods are configured and implemented in a similar way.

Availability zone
This enables you to arrange OpenStack compute hosts into logical groups and provides a form of physical isolation and redundancy from other availability zones, such as by using a separate power supply or network equipment.

You define the availability zone in which a specified compute host resides locally on each server. An availability zone is commonly used to identify a set of servers that have a common attribute. For instance, if some of the racks in your data center are on a separate power source, you can put servers in those racks in their own availability zone. Availability zones can also help separate different classes of hardware.

When users provision resources, they can specify from which availability zone they want their instance to be built. This allows cloud consumers to ensure that their application resources are spread across disparate machines to achieve high availability in the event of hardware failure.

Host aggregates zone
This enables you to partition OpenStack Compute deployments into logical groups for load balancing and instance distribution. You can use host aggregates to further partition an availability zone. For example, you might use host aggregates to partition an availability zone into groups of hosts that either share common resources, such as storage and network, or have a special property, such as trusted computing hardware.

A common use of host aggregates is to provide information for use with the. For example, you might use a host aggregate to group a set of hosts that share specific flavors or images.

The general case for this is setting key-value pairs in the aggregate metadata and matching key-value pairs in flavor’s  metadata. The  in the filter scheduler will enforce that instances be scheduled only on hosts in aggregates that define the same key to the same value.

An advanced use of this general concept allows different flavor types to run with different CPU and RAM allocation ratios so that high-intensity computing loads and low-intensity development and testing systems can share the same cloud without either starving the high-use systems or wasting resources on low-utilization systems. This works by setting  in your host aggregates and matching   in your flavor types.

The first step is setting the aggregate metadata keys  and   to a floating-point value. The filter schedulers  and   will use those values rather than the global defaults in   when scheduling to hosts in the aggregate. Be cautious when using this feature, since each host can be in multiple aggregates, but should have only one allocation ratio for each resources. It is up to you to avoid putting a host in multiple aggregates that define different values for the same resource.

This is the first half of the equation. To get flavor types that are guaranteed a particular ratio, you must set the  in the flavor type to the key-value pair you want to match in the aggregate. For example, if you define    to “1.0”, then instances of that type will run in aggregates only where the metadata key   is also defined as “1.0.” In practice, it is better to define an additional key-value pair in the aggregate metadata to match on rather than match directly on   or. This allows better abstraction. For example, by defining a key  and setting a value of “high,” “medium,” or “low,” you could then tune the numeric allocation ratios in the aggregates without also needing to change all flavor types relating to them.

Scalable Hardware
While several resources already exist to help with deploying and installing OpenStack, it’s very important to make sure that you have your deployment planned out ahead of time. This guide presumes that you have set aside a rack for the OpenStack cloud but also offers suggestions for when and what to scale.

Hardware Procurement
“The Cloud” has been described as a volatile environment where servers can be created and terminated at will. While this may be true, it does not mean that your servers must be volatile. Ensuring that your cloud’s hardware is stable and configured correctly means that your cloud environment remains up and running.

OpenStack can be deployed on any hardware supported by an OpenStack compatible Linux distribution.

Hardware does not have to be consistent, but it should at least have the same type of CPU to support instance migration.

The typical hardware recommended for use with OpenStack is the standard value-for-money offerings that most hardware vendors stock. It should be straightforward to divide your procurement into building blocks such as “compute,” “object storage,” and “cloud controller,” and request as many of these as you need. Alternatively, any existing servers you have that meet performance requirements and virtualization technology are likely to support OpenStack.

Capacity Planning
OpenStack is designed to increase in size in a straightforward manner. Taking into account the considerations previous mentioned, particularly on the sizing of the cloud controller, it should be possible to procure additional compute or object storage nodes as needed. New nodes do not need to be the same specification or vendor as existing nodes.

For compute nodes,  will manage differences in sizing with core count and RAM. However, you should consider that the user experience changes with differing CPU speeds. When adding object storage nodes, a weight should be specified that reflects the capability of the node.

Monitoring the resource usage and user growth will enable you to know when to procure. The Logging and Monitoring chapte in the Operations Guide details some useful metrics.

Burn-in Testing
The chances of failure for the server’s hardware are high at the start and the end of its life. As a result, dealing with hardware failures while in production can be avoided by appropriate burn-in testing to attempt to trigger the early-stage failures. The general principle is to stress the hardware to its limits. Examples of burn-in tests include running a CPU or disk benchmark for several days.

Typical tests include


 * CPU - burnP6, burnK7, burnMMX
 * Disk - badblocks, S.M.A.R.T. self-test, fsprobe
 * RAM - memtester
 * Network - netperf

A BMC SEL log can also be monitored to ensure no events such as ECC errors occur during the test period.