374d0fa214
1. Added new directory for compute concepts 2. Populated sections with existing content 3. Removed old compute-design-tech section 4. Added landing page for compute to mimic networking and storage Change-Id: I9633b1d8bd30194026fcaf71b6335fac54e946d2 Implements: blueprint arch-guide-restructure
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Overview
When designing compute resource pools, consider the number of processors, amount of memory, and the quantity of storage required for each hypervisor.
Determine whether compute resources will be provided in a single pool or in multiple pools. In most cases, multiple pools of resources can be allocated and addressed on demand, commonly referred to as bin packing.
In a bin packing design, each independent resource pool provides service for specific flavors. Since instances are scheduled onto compute hypervisors, each independent node's resources will be allocated to efficiently use the available hardware. Bin packing also requires a common hardware design, with all hardware nodes within a compute resource pool sharing a common processor, memory, and storage layout. This makes it easier to deploy, support, and maintain nodes throughout their lifecycle.
Increasing the size of the supporting compute environment increases the network traffic and messages, adding load to the controller or networking nodes. Effective monitoring of the environment will help with capacity decisions on scaling.
Compute nodes automatically attach to OpenStack clouds, resulting in a horizontally scaling process when adding extra compute capacity to an OpenStack cloud. Additional processes are required to place nodes into appropriate availability zones and host aggregates. When adding additional compute nodes to environments, ensure identical or functional compatible CPUs are used, otherwise live migration features will break. It is necessary to add rack capacity or network switches as scaling out compute hosts directly affects data center resources.
Compute host components can also be upgraded to account for increases in demand, known as vertical scaling. Upgrading CPUs with more cores, or increasing the overall server memory, can add extra needed capacity depending on whether the running applications are more CPU intensive or memory intensive.
When selecting a processor, compare features and performance characteristics. Some processors include features specific to virtualized compute hosts, such as hardware-assisted virtualization, and technology related to memory paging (also known as EPT shadowing). These types of features can have a significant impact on the performance of your virtual machine.
The number of processor cores and threads impacts the number of worker threads which can be run on a resource node. Design decisions must relate directly to the service being run on it, as well as provide a balanced infrastructure for all services.
Another option is to assess the average workloads and increase the number of instances that can run within the compute environment by adjusting the overcommit ratio. This ratio is configurable for CPU and memory. The default CPU overcommit ratio is 16:1, and the default memory overcommit ratio is 1.5:1. Determining the tuning of the overcommit ratios during the design phase is important as it has a direct impact on the hardware layout of your compute nodes.
Note
Changing the CPU overcommit ratio can have a detrimental effect and cause a potential increase in a noisy neighbor.
Insufficient disk capacity could also have a negative effect on overall performance including CPU and memory usage. Depending on the back-end architecture of the OpenStack Block Storage layer, capacity includes adding disk shelves to enterprise storage systems or installing additional Block Storage nodes. Upgrading directly attached storage installed in Compute hosts, and adding capacity to the shared storage for additional ephemeral storage to instances, may be necessary.
Consider the Compute requirements of non-hypervisor nodes (also referred to as resource nodes). This includes controller, Object Storage nodes, Block Storage nodes, and networking services.
The ability to add Compute resource pools for unpredictable workloads should be considered. In some cases, the demand for certain instance types or flavors may not justify individual hardware design. Allocate hardware designs that are capable of servicing the most common instance requests. Adding hardware to the overall architecture can be done later.