In this chapter, we discuss some of the choices you need to consider when building out your compute nodes. Compute nodes form the resource core of the OpenStack Compute cloud, providing the processing, memory, network and storage resources to run instances.

The type of CPU in your compute node is a very important choice. First, ensure that the CPU supports virtualization by way of VT-x for Intel chips and AMD-v for AMD chips. CPUs (central processing units) choosing compute nodes CPU choice Consult the vendor documentation to check for virtualization support. For Intel, read “Does my processor support Intel® Virtualization Technology?”. For AMD, read AMD Virtualization. Note that your CPU may support virtualization but it may be disabled. Consult your BIOS documentation for how to enable CPU features. virtualization technology AMD Virtualization Intel Virtualization Technology The number of cores that the CPU has also affects the decision. It's common for current CPUs to have up to 12 cores. Additionally, if an Intel CPU supports hyperthreading, those 12 cores are doubled to 24 cores. If you purchase a server that supports multiple CPUs, the number of cores is further multiplied. cores hyperthreading multithreading Hyper-Threading is Intel's proprietary simultaneous multithreading implementation used to improve parallelization on their CPUs. You might consider enabling Hyper-Threading to improve the performance of multithreaded applications. Whether you should enable Hyper-Threading on your CPUs depends upon your use case. For example, disabling Hyper-Threading can be beneficial in intense computing environments. We recommend that you do performance testing with your local workload with both Hyper-Threading on and off to determine what is more appropriate in your case. CPUs (central processing units) enabling hyperthreading on

A hypervisor provides software to manage virtual machine access to the underlying hardware. The hypervisor creates, manages, and monitors virtual machines. Docker Hyper-V ESXi hypervisor ESX hypervisor VMware API Quick EMUlator (QEMU) Linux containers (LXC) kernel-based VM (KVM) hypervisor Xen API XenServer hypervisor hypervisors choosing compute nodes hypervisor choice OpenStack Compute supports many hypervisors to various degrees, including: KVM LXC QEMU VMWare ESX/ESXi Xen Hyper-V Docker Probably the most important factor in your choice of hypervisor is your current usage or experience. Aside from that, there are practical concerns to do with feature parity, documentation, and the level of community experience. For example, KVM is the most widely adopted hypervisor in the OpenStack community. Besides KVM, more deployments run Xen, LXC, VMWare, and Hyper-V than the others listed. However, each of these are lacking some feature support or the documentation on how to use them with OpenStack is out of date. The best information available to support your choice is found on the Hypervisor Support Matrix and in the configuration reference. It is also possible to run multiple hypervisors in a single deployment using host aggregates or cells. However, an individual compute node can run only a single hypervisor at a time. hypervisors running multiple

As part of the procurement for a compute cluster, you must specify some storage for the disk on which the instantiated instance runs. There are three main approaches to providing this temporary-style storage, and it is important to understand the implications of the choice. storage instance storage solutions instances storage solutions compute nodes instance storage solutions They are: Off compute node storage—shared file system On compute node storage—shared file system On compute node storage—nonshared file system In general, the questions you should ask when selecting storage are as follows: What is the platter count you can achieve? Do more spindles result in better I/O despite network access? Which one results in the best cost-performance scenario you're aiming for? How do you manage the storage operationally? Many operators use separate compute and storage hosts. Compute services and storage services have different requirements, and compute hosts typically require more CPU and RAM than storage hosts. Therefore, for a fixed budget, it makes sense to have different configurations for your compute nodes and your storage nodes. Compute nodes will be invested in CPU and RAM, and storage nodes will be invested in block storage. However, if you are more restricted in the number of physical hosts you have available for creating your cloud and you want to be able to dedicate as many of your hosts as possible to running instances, it makes sense to run compute and storage on the same machines. We'll discuss the three main approaches to instance storage in the next few sections.

In this option, the disks storing the running instances are hosted in servers outside of the compute nodes. shared storage file systems shared If you use separate compute and storage hosts, you can treat your compute hosts as "stateless." As long as you don't have any instances currently running on a compute host, you can take it offline or wipe it completely without having any effect on the rest of your cloud. This simplifies maintenance for the compute hosts. There are several advantages to this approach: If a compute node fails, instances are usually easily recoverable. Running a dedicated storage system can be operationally simpler. You can scale to any number of spindles. It may be possible to share the external storage for other purposes. The main downsides to this approach are: Depending on design, heavy I/O usage from some instances can affect unrelated instances. Use of the network can decrease performance.

In this option, each compute node is specified with a significant amount of disk space, but a distributed file system ties the disks from each compute node into a single mount. The main advantage of this option is that it scales to external storage when you require additional storage. However, this option has several downsides: Running a distributed file system can make you lose your data locality compared with nonshared storage. Recovery of instances is complicated by depending on multiple hosts. The chassis size of the compute node can limit the number of spindles able to be used in a compute node. Use of the network can decrease performance.

In this option, each compute node is specified with enough disks to store the instances it hosts. file systems nonshared There are two main reasons why this is a good idea: Heavy I/O usage on one compute node does not affect instances on other compute nodes. Direct I/O access can increase performance. This has several downsides: If a compute node fails, the instances running on that node are lost. The chassis size of the compute node can limit the number of spindles able to be used in a compute node. Migrations of instances from one node to another are more complicated and rely on features that may not continue to be developed. If additional storage is required, this option does not scale. Running a shared file system on a storage system apart from the computes nodes is ideal for clouds where reliability and scalability are the most important factors. Running a shared file system on the compute nodes themselves may be best in a scenario where you have to deploy to preexisting servers for which you have little to no control over their specifications. Running a nonshared file system on the compute nodes themselves is a good option for clouds with high I/O requirements and low concern for reliability. scaling file system choice

We consider live migration an integral part of the operations of the cloud. This feature provides the ability to seamlessly move instances from one physical host to another, a necessity for performing upgrades that require reboots of the compute hosts, but only works well with shared storage. storage live migration migration live migration compute nodes live migration Live migration can also be done with nonshared storage, using a feature known as KVM live block migration. While an earlier implementation of block-based migration in KVM and QEMU was considered unreliable, there is a newer, more reliable implementation of block-based live migration as of QEMU 1.4 and libvirt 1.0.2 that is also compatible with OpenStack. However, none of the authors of this guide have first-hand experience using live block migration. block migration

If you want to support shared-storage live migration, you need to configure a distributed file system. compute nodes file system choice file systems choice of storage file system choice Possible options include: NFS (default for Linux) GlusterFS MooseFS Lustre We've seen deployments with all, and recommend that you choose the one you are most familiar with operating. If you are not familiar with any of these, choose NFS, as it is the easiest to set up and there is extensive community knowledge about it.

OpenStack allows you to overcommit CPU and RAM on compute nodes. This allows you to increase the number of instances you can have running on your cloud, at the cost of reducing the performance of the instances. RAM overcommit CPUs (central processing units) overcommitting overcommitting compute nodes overcommitting OpenStack Compute uses the following ratios by default: CPU allocation ratio: 16:1 RAM allocation ratio: 1.5:1 The default CPU allocation ratio of 16:1 means that the scheduler allocates up to 16 virtual cores per physical core. For example, if a physical node has 12 cores, the scheduler sees 192 available virtual cores. With typical flavor definitions of 4 virtual cores per instance, this ratio would provide 48 instances on a physical node. The formula for the number of virtual instances on a compute node is (OR*PC)/VC, where: OR CPU overcommit ratio (virtual cores per physical core) PC Number of physical cores VC Number of virtual cores per instance Similarly, the default RAM allocation ratio of 1.5:1 means that the scheduler allocates instances to a physical node as long as the total amount of RAM associated with the instances is less than 1.5 times the amount of RAM available on the physical node. For example, if a physical node has 48 GB of RAM, the scheduler allocates instances to that node until the sum of the RAM associated with the instances reaches 72 GB (such as nine instances, in the case where each instance has 8 GB of RAM). You must select the appropriate CPU and RAM allocation ratio for your particular use case.

Logging is detailed more fully in . However, it is an important design consideration to take into account before commencing operations of your cloud. logging/monitoring compute nodes and compute nodes logging OpenStack produces a great deal of useful logging information, however; but for the information to be useful for operations purposes, you should consider having a central logging server to send logs to, and a log parsing/analysis system (such as logstash).

Networking in OpenStack is a complex, multifaceted challenge. See . compute nodes networking

Compute nodes are the workhorse of your cloud and the place where your users' applications will run. They are likely to be affected by your decisions on what to deploy and how you deploy it. Their requirements should be reflected in the choices you make.