Resources available for your workloads

This page shows how to determine how much CPU and memory is available on a node to run your workloads on Google Distributed Cloud.

Reserved resources

On each cluster node, Google Distributed Cloud reserves the following resources for operating system components and core Kubernetes components:

  • 80 millicores + 1% of the CPU capacity
  • 330 MiB + 5% of the memory capacity

For example, suppose a node has the default capacity of 4 CPU cores and 8 GiB of memory. Then Google Distributed Cloud reserves:

  • 80 millicores + 1% of 4 cores = 120 millicores
  • 330 MiB + 5% of 8 GiB = 730 MiB

The operating system and core Kubernetes components do not run as Pods; they run as ordinary processes. The resources that remain, beyond these reserved resources, are available for Pods.

Eviction threshold

To determine how much memory is available for Pods, you must also consider the eviction threshold. Google Distributed Cloud sets an eviction threshold of 100 MiB. This means that if available memory on a node falls below 100 MiB, the kubelet might evict one or more Pods.

Allocatable resources

The resources on a node that are available for Pods are called the allocatable resources. Calculate the allocatable resources as follows:

  • Allocatable CPU = CPU Capacity - Reserved CPU
  • Allocatable Memory = Memory Capacity - Reserved Memory - Eviction Threshold

For example, suppose a node has 8 GiB of memory capacity, 680 MiB of reserved memory, and an eviction threshold of 100 MiB. Then the allocatable memory is:

8 GiB - 680 MiB - 100 MiB = 7220 MiB

Resources available for your workloads

A node's allocatable resources are the resources available for Pods. This includes the Pods that run your workloads and the Pods that run Google Distributed Cloud add-ons. Add-ons include the ingress controller, the ingress service, the Connect agent, networking components, logging components, and more.

On a given node, to determine the resources available for your workloads, start with the allocatable resources and then subtract the resources used by add-ons.

The challenge is that add-ons are not distributed evenly among the nodes of a Google Distributed Cloud cluster. One node might have three add-ons, and another node might have ten add-ons. Also, the various add-ons require different amounts of CPU and memory.

As a general rule, you can figure that the add-ons running on a node require:

  • 200 millicores of CPU
  • 100 MiB of memory

Now you can calculate the resources available on a node for your workloads as follows:

  • Allocatable CPU - 200 millicores
  • Allocatable memory - 100 MiB

Certain nodes require more resources for add-ons than the preceding general rule indicates. For example, one node might run a Prometheus add-on that requires 2 GiB of memory. But if your cluster has more than a few nodes, it is reasonable to assume that the general rule applies to most nodes.

What's next

To learn more about the concept of allocatable resources, see Allocatable resources in the documentation for GKE on Google Cloud.