0.5% of the next 2 cores (up to 4 cores)Ī virtual machine of type n1-standard-2 has 2 vCPU and 7.5GB of memory.Īccording to the above rules the CPU reserved is:Īllocatable memory = 0.25 * 4 (first 4GB) + 0.2 * 3.5 (remaining 3.5GB).

6% of the next 112GB of memory (up to 128GB)įor CPU resources, GKE reserves the following:.

10% of the next 8GB of memory (up to 16GB).

20% of the next 4GB of memory (up to 8GB).

255 MiB of memory for machines with less than 1 GB of memory.

Google Kubernetes Engine (GKE) has a well-defined list of rules to assign memory and CPU to a Node.įor memory resources, GKE reserves the following: However, there's consensus in the major managed Kubernetes services Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS), and Elastic Kubernetes Service (EKS), and it's worth discussing how they partition the available resources. Unfortunately, there isn't a fixed answer as it depends on your cluster. It could use more (or less) resources - you're just allocating and estimating memory and CPU usage at best of your abilities.īut how do you decide how to assign resources?

Resources reserved to the eviction threshold.Īs you can guess, all of those quotas are customisable.īut please notice that reserving 100MB of memory for the operating system doesn't mean that the OS is limited to use only that amount.

Resources necessary to run Kubernetes agents such as the Kubelet, the container runtime, node problem detector, etc.

Resources needed to run the operating system and system daemons such as SSH, systemd, etc.

If you look closely at a single Node, you can divide the available resources in: The operating system and the kubelet require memory and CPU too, and you should cater for those extra resources. However, not all resources in a Node can be used to run Pods. Pods deployed in your Kubernetes cluster consume resources such as memory, CPU and storage. How resources are allocated in cluster nodes The infographic below summarises how memory and CPU are allocated in Google Kubernetes Engine (GKE), Elastic Kubernetes Service (EKS) and Azure Kubernetes Service (AKS).

TL DR: Not all CPU and memory in your Kubernetes nodes can be used to run Pods.

K8 resources