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Sizing guidance for hosted control planes

Many factors, including hosted cluster workload and worker node count, affect how many hosted clusters can fit within a certain number of control-plane nodes. Use this sizing guide to help with hosted cluster capacity planning. This guidance assumes a highly available hosted control planes topology. The load-based sizing examples were measured on a bare-metal cluster. Cloud-based instances might have different limiting factors, such as memory size.

You can override the following resource utilization sizing measurements and disable the metric service monitoring.

See the following highly available hosted control planes requirements, which were tested with OKD version 4.12.9 and later:

  • 78 pods

  • Three 8 GiB PVs for etcd

  • Minimum vCPU: approximately 5.5 cores

  • Minimum memory: approximately 19 GiB

Additional resources

Pod limits

The maxPods setting for each node affects how many hosted clusters can fit in a control-plane node. It is important to note the maxPods value on all control-plane nodes. Plan for about 75 pods for each highly available hosted control plane.

For bare-metal nodes, the default maxPods setting of 250 is likely to be a limiting factor because roughly three hosted control planes fit for each node given the pod requirements, even if the machine has plenty of resources to spare. Setting the maxPods value to 500 by configuring the KubeletConfig value allows for greater hosted control plane density, which can help you take advantage of additional compute resources.

Additional resources

Request-based resource limit

The maximum number of hosted control planes that the cluster can host is calculated based on the hosted control plane CPU and memory requests from the pods.

A highly available hosted control plane consists of 78 pods that request 5 vCPUs and 18 GB memory. These baseline numbers are compared to the cluster worker node resource capacities to estimate the maximum number of hosted control planes.

Load-based limit

The maximum number of hosted control planes that the cluster can host is calculated based on the hosted control plane pods CPU and memory utilizations when some workload is put on the hosted control plane Kubernetes API server.

The following method is used to measure the hosted control plane resource utilizations as the workload increases:

  • A hosted cluster with 9 workers that use 8 vCPU and 32 GiB each, while using the KubeVirt platform

  • The workload test profile that is configured to focus on API control-plane stress, based on the following definition:

    • Created objects for each namespace, scaling up to 100 namespaces total

    • Additional API stress with continuous object deletion and creation

    • Workload queries-per-second (QPS) and Burst settings set high to remove any client-side throttling

As the load increases by 1000 QPS, the hosted control plane resource utilization increases by 9 vCPUs and 2.5 GB memory.

For general sizing purposes, consider the 1000 QPS API rate that is a medium hosted cluster load, and a 2000 QPS API that is a heavy hosted cluster load.

This test provides an estimation factor to increase the compute resource utilization based on the expected API load. Exact utilization rates can vary based on the type and pace of the cluster workload.
Table 1. Load table
Hosted control plane resource utilization scaling vCPUs Memory (GiB)

Resource utilization with no load

2.9

11.1

Resource utilization with 1000 QPS

9.0

2.5

As the load increases by 1000 QPS, the hosted control plane resource utilization increases by 9 vCPUs and 2.5 GB memory.

For general sizing purposes, consider a 1000 QPS API rate to be a medium hosted cluster load and a 2000 QPS API to be a heavy hosted cluster load.

The following example shows hosted control plane resource scaling for the workload and API rate definitions:

Table 2. API rate table
QPS (API rate) vCPU usage Memory usage (GiB)

Low load (Less than 50 QPS)

2.9

11.1

Medium load (1000 QPS)

11.9

13.6

High load (2000 QPS)

20.9

16.1

The hosted control plane sizing is about control-plane load and workloads that cause heavy API activity, etcd activity, or both. Hosted pod workloads that focus on data-plane loads, such as running a database, might not result in high API rates.

Sizing calculation example

This example provides sizing guidance for the following scenario:

  • Three bare-metal workers that are labeled as hypershift.openshift.io/control-plane nodes

  • maxPods value set to 500

  • The expected API rate is medium or about 1000, according to the load-based limits

Table 3. Limit inputs
Limit description Server 1 Server 2

Number of vCPUs on worker node

64

128

Memory on worker node (GiB)

128

256

Maximum pods per worker

500

500

Number of workers used to host control planes

3

3

Maximum QPS target rate (API requests per second)

1000

1000
Table 4. Sizing calculation example

Calculated values based on worker node size and API rate

Server 1

Server 2

Calculation notes

Maximum hosted control planes per worker based on vCPU requests

12.8

25.6

Number of worker vCPUs ÷ 5 total vCPU requests per hosted control plane

Maximum hosted control planes per worker based on vCPU usage

5.4

10.7

Number of vCPUS ÷ (2.9 measured idle vCPU usage + (QPS target rate ÷ 1000) × 9.0 measured vCPU usage per 1000 QPS increase)

Maximum hosted control planes per worker based on memory requests

7.1

14.2

Worker memory GiB ÷ 18 GiB total memory request per hosted control plane

Maximum hosted control planes per worker based on memory usage

9.4

18.8

Worker memory GiB ÷ (11.1 measured idle memory usage + (QPS target rate ÷ 1000) × 2.5 measured memory usage per 1000 QPS increase)

Maximum hosted control planes per worker based on per node pod limit

6.7

6.7

500 maxPods ÷ 75 pods per hosted control plane

Minimum of previously mentioned maximums

5.4

6.7

vCPU limiting factor

maxPods limiting factor

Maximum number of hosted control planes within a management cluster

16

20

Minimum of previously mentioned maximums × 3 control-plane workers
Table 5. Hosted control planes capacity metrics

Name

Description

mce_hs_addon_request_based_hcp_capacity_gauge

Estimated maximum number of hosted control planes the cluster can host based on a highly available hosted control planes resource request.

mce_hs_addon_low_qps_based_hcp_capacity_gauge

Estimated maximum number of hosted control planes the cluster can host if all hosted control planes make around 50 QPS to the clusters Kube API server.

mce_hs_addon_medium_qps_based_hcp_capacity_gauge

Estimated maximum number of hosted control planes the cluster can host if all hosted control planes make around 1000 QPS to the clusters Kube API server.

mce_hs_addon_high_qps_based_hcp_capacity_gauge

Estimated maximum number of hosted control planes the cluster can host if all hosted control planes make around 2000 QPS to the clusters Kube API server.

mce_hs_addon_average_qps_based_hcp_capacity_gauge

Estimated maximum number of hosted control planes the cluster can host based on the existing average QPS of hosted control planes. If you do not have an active hosted control planes, you can expect low QPS.