Appendix A. Appendix

A.1. Compute CPU and memory calculator

The following subsections describe how the OpenStack Workflow calculates the optimal settings for CPU and memory.

A.1.1. NovaReservedHostMemory

The NovaReservedHostMemory parameter sets the amount of memory (in MB) to reserve for the host node. To determine an appropriate value for hyper-converged nodes, assume that each OSD consumes 3 GB of memory. Given a node with 256 GB memory and 10 OSDs, you can allocate 30 GB of memory for Ceph, leaving 226 GB for Compute. With that much memory a node can host, for example, 113 instances using 2 GB of memory each.

However, you still need to consider additional overhead per instance for the hypervisor. Assuming this overhead is 0.5 GB, the same node can only host 90 instances, which accounts for the 226 GB divided by 2.5 GB. The amount of memory to reserve for the host node (that is, memory the Compute service should not use) is:

(In * Ov) + (Os * RA)

Where:

  • In: number of instances
  • Ov: amount of overhead memory needed per instance
  • Os: number of OSDs on the node
  • RA: amount of RAM that each OSD should have

With 90 instances, this give us (90*0.5) + (10*3) = 75 GB. The Compute service expects this value in MB, namely 75000.

The following Python code provides this computation:

left_over_mem = mem - (GB_per_OSD * osds)
number_of_guests = int(left_over_mem /
    (average_guest_size + GB_overhead_per_guest))
nova_reserved_mem_MB = MB_per_GB * (
    (GB_per_OSD * osds) +
    (number_of_guests * GB_overhead_per_guest))

A.1.2. cpu_allocation_ratio

The Compute scheduler uses cpu_allocation_ratio when choosing which Compute nodes on which to deploy an instance. By default, this is 16.0 (as in, 16:1). This means if there are 56 cores on a node, the Compute scheduler will schedule enough instances to consume 896 vCPUs on a node before considering the node unable to host any more.

To determine a suitable cpu_allocation_ratio for a hyper-converged node, assume each Ceph OSD uses at least one core (unless the workload is I/O-intensive, and on a node with no SSD). On a node with 56 cores and 10 OSDs, this would leave 46 cores for Compute. If each instance uses 100 per cent of the CPU it receives, then the ratio would simply be the number of instance vCPUs divided by the number of cores; that is, 46 / 56 = 0.8. However, since instances do not normally consume 100 per cent of their allocated CPUs, you can raise the cpu_allocation_ratio by taking the anticipated percentage into account when determining the number of required guest vCPUs.

So, if we can predict that instances will only use 10 per cent (or 0.1) of their vCPU, then the number of vCPUs for instances can be expressed as 46 / 0.1 = 460. When this value is divided by the number of cores (56), the ratio increases to approximately 8.

The following Python code provides this computation:

cores_per_OSD = 1.0
average_guest_util = 0.1 # 10%
nonceph_cores = cores - (cores_per_OSD * osds)
guest_vCPUs = nonceph_cores / average_guest_util
cpu_allocation_ratio = guest_vCPUs / cores