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9.1 KiB
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215 lines
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Technical considerations
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In a compute-focused OpenStack cloud, the type of instance workloads you
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provision heavily influences technical decision making.
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Public and private clouds require deterministic capacity planning to
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support elastic growth in order to meet user SLA expectations.
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Deterministic capacity planning is the path to predicting the effort and
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expense of making a given process perform consistently. This process is
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important because, when a service becomes a critical part of a user's
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infrastructure, the user's experience links directly to the SLAs of the
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cloud itself.
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There are two aspects of capacity planning to consider:
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* Planning the initial deployment footprint
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* Planning expansion of the environment to stay ahead of cloud user demands
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Begin planning an initial OpenStack deployment footprint with
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estimations of expected uptake, and existing infrastructure workloads.
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The starting point is the core count of the cloud. By applying relevant
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ratios, the user can gather information about:
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* The number of expected concurrent instances: (overcommit fraction ×
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cores) / virtual cores per instance
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* Required storage: flavor disk size × number of instances
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These ratios determine the amount of additional infrastructure needed to
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support the cloud. For example, consider a situation in which you
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require 1600 instances, each with 2 vCPU and 50 GB of storage. Assuming
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the default overcommit rate of 16:1, working out the math provides an
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equation of:
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* 1600 = (16 × (number of physical cores)) / 2
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* Storage required = 50 GB × 1600
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On the surface, the equations reveal the need for 200 physical cores and
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80 TB of storage for ``/var/lib/nova/instances/``. However, it is also
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important to look at patterns of usage to estimate the load that the API
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services, database servers, and queue servers are likely to encounter.
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Aside from the creation and termination of instances, consider the
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impact of users accessing the service, particularly on nova-api and its
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associated database. Listing instances gathers a great deal of
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information and given the frequency with which users run this operation,
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a cloud with a large number of users can increase the load
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significantly. This can even occur unintentionally. For example, the
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OpenStack Dashboard instances tab refreshes the list of instances every
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30 seconds, so leaving it open in a browser window can cause unexpected
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load.
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Consideration of these factors can help determine how many cloud
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controller cores you require. A server with 8 CPU cores and 8 GB of RAM
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server would be sufficient for a rack of compute nodes, given the above
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caveats.
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Key hardware specifications are also crucial to the performance of user
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instances. Be sure to consider budget and performance needs, including
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storage performance (spindles/core), memory availability (RAM/core),
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network bandwidth (Gbps/core), and overall CPU performance (CPU/core).
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The cloud resource calculator is a useful tool in examining the impacts
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of different hardware and instance load outs. See `cloud-resource-calculator
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<https://github.com/noslzzp/cloud-resource-calculator/blob/master/cloud-resource-calculator.ods>`_.
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Expansion planning
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~~~~~~~~~~~~~~~~~~
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A key challenge for planning the expansion of cloud compute services is
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the elastic nature of cloud infrastructure demands.
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Planning for expansion is a balancing act. Planning too conservatively
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can lead to unexpected oversubscription of the cloud and dissatisfied
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users. Planning for cloud expansion too aggressively can lead to
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unexpected underuse of the cloud and funds spent unnecessarily
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on operating infrastructure.
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The key is to carefully monitor the trends in cloud usage over time. The
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intent is to measure the consistency with which you deliver services,
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not the average speed or capacity of the cloud. Using this information
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to model capacity performance enables users to more accurately determine
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the current and future capacity of the cloud.
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CPU and RAM
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~~~~~~~~~~~
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OpenStack enables users to overcommit CPU and RAM on compute nodes. This
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allows an increase in the number of instances running on the cloud at
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the cost of reducing the performance of the instances. OpenStack Compute
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uses the following ratios by default:
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* CPU allocation ratio: 16:1
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* RAM allocation ratio: 1.5:1
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The default CPU allocation ratio of 16:1 means that the scheduler
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allocates up to 16 virtual cores per physical core. For example, if a
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physical node has 12 cores, the scheduler sees 192 available virtual
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cores. With typical flavor definitions of 4 virtual cores per instance,
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this ratio would provide 48 instances on a physical node.
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Similarly, the default RAM allocation ratio of 1.5:1 means that the
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scheduler allocates instances to a physical node as long as the total
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amount of RAM associated with the instances is less than 1.5 times the
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amount of RAM available on the physical node.
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You must select the appropriate CPU and RAM allocation ratio based on
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particular use cases.
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Additional hardware
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~~~~~~~~~~~~~~~~~~~
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Certain use cases may benefit from exposure to additional devices on the
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compute node. Examples might include:
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* High performance computing jobs that benefit from the availability of
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graphics processing units (GPUs) for general-purpose computing.
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* Cryptographic routines that benefit from the availability of hardware
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random number generators to avoid entropy starvation.
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* Database management systems that benefit from the availability of
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SSDs for ephemeral storage to maximize read/write time.
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Host aggregates group hosts that share similar characteristics, which
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can include hardware similarities. The addition of specialized hardware
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to a cloud deployment is likely to add to the cost of each node, so
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consider carefully whether all compute nodes, or just a subset targeted
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by flavors, need the additional customization to support the desired
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workloads.
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Utilization
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~~~~~~~~~~~
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Infrastructure-as-a-Service offerings, including OpenStack, use flavors
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to provide standardized views of virtual machine resource requirements
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that simplify the problem of scheduling instances while making the best
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use of the available physical resources.
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In order to facilitate packing of virtual machines onto physical hosts,
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the default selection of flavors provides a second largest flavor that
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is half the size of the largest flavor in every dimension. It has half
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the vCPUs, half the vRAM, and half the ephemeral disk space. The next
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largest flavor is half that size again. The following figure provides a
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visual representation of this concept for a general purpose computing
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design:
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.. figure:: figures/Compute_Tech_Bin_Packing_General1.png
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The following figure displays a CPU-optimized, packed server:
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.. figure:: figures/Compute_Tech_Bin_Packing_CPU_optimized1.png
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These default flavors are well suited to typical configurations of
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commodity server hardware. To maximize utilization, however, it may be
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necessary to customize the flavors or create new ones in order to better
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align instance sizes to the available hardware.
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Workload characteristics may also influence hardware choices and flavor
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configuration, particularly where they present different ratios of CPU
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versus RAM versus HDD requirements.
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For more information on Flavors see `OpenStack Operations Guide:
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Flavors <http://docs.openstack.org/ops-guide/ops-user-facing-operations.html#flavors>`_.
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OpenStack components
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~~~~~~~~~~~~~~~~~~~~
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Due to the nature of the workloads in this scenario, a number of
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components are highly beneficial for a Compute-focused cloud. This
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includes the typical OpenStack components:
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* :term:`Compute service (nova)`
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* :term:`Image service` (glance)
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* :term:`Identity service (keystone)`
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Also consider several specialized components:
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* :term:`Orchestration service (heat)`
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Given the nature of the applications involved in this scenario, these
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are heavily automated deployments. Making use of Orchestration is
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highly beneficial in this case. You can script the deployment of a
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batch of instances and the running of tests, but it makes sense to
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use the Orchestration service to handle all these actions.
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* :term:`Telemetry service (telemetry)`
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Telemetry and the alarms it generates support autoscaling of
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instances using Orchestration. Users that are not using the
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Orchestration service do not need to deploy the Telemetry service and
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may choose to use external solutions to fulfill their metering and
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monitoring requirements.
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* :term:`Block Storage service (cinder)`
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Due to the burst-able nature of the workloads and the applications
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and instances that perform batch processing, this cloud mainly uses
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memory or CPU, so the need for add-on storage to each instance is not
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a likely requirement. This does not mean that you do not use
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OpenStack Block Storage (cinder) in the infrastructure, but typically
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it is not a central component.
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* :term:`Networking service (neutron)`
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When choosing a networking platform, ensure that it either works with
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all desired hypervisor and container technologies and their OpenStack
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drivers, or that it includes an implementation of an ML2 mechanism
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driver. You can mix networking platforms that provide ML2 mechanisms
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drivers.
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