Node-pressure eviction is the process by which the kubelet proactively terminates pods to reclaim resources on nodes. Note: FEATURE STATE: Kubernetes v1.31 [beta] (enabled by default: true) The split image filesystem feature, which enables support for the containerfs filesystem, adds several new eviction signals, thresholds and metrics. To use containerfs, the Kubernetes release v1.33 requires the KubeletSeparateDiskGC feature gate to be enabled. Currently, only CRI-O (v1.29 or higher) offers the containerfs filesystem support.

FEATURE STATE: Kubernetes v1.32 [beta] (enabled by default: false) Dynamic resource allocation is an API for requesting and sharing resources between pods and containers inside a pod. It is a generalization of the persistent volumes API for generic resources. Typically those resources are devices like GPUs. Third-party resource drivers are responsible for tracking and preparing resources, with allocation of resources handled by Kubernetes via structured parameters (introduced in Kubernetes 1.

In the scheduling-plugin NodeResourcesFit of kube-scheduler, there are two scoring strategies that support the bin packing of resources: MostAllocated and RequestedToCapacityRatio. Enabling bin packing using MostAllocated strategy The MostAllocated strategy scores the nodes based on the utilization of resources, favoring the ones with higher allocation. For each resource type, you can set a weight to modify its influence in the node score. To set the MostAllocated strategy for the NodeResourcesFit plugin, use a scheduler configuration similar to the following:

Node Problem Detector is a daemon for monitoring and reporting about a node's health. You can run Node Problem Detector as a DaemonSet or as a standalone daemon. Node Problem Detector collects information about node problems from various daemons and reports these conditions to the API server as Node Conditions or as Events. To learn how to install and use Node Problem Detector, see Node Problem Detector project documentation.

FEATURE STATE: Kubernetes v1.24 [stable] In this example, you will run a Kubernetes Job that uses multiple parallel worker processes. Each worker is a different container running in its own Pod. The Pods have an index number that the control plane sets automatically, which allows each Pod to identify which part of the overall task to work on. The pod index is available in the annotation batch.kubernetes.io/job-completion-index as a string representing its decimal value.

Configure and manage huge pages as a schedulable resource in a cluster.

This page shows how to perform a rolling update on a DaemonSet. Before you begin You need to have a Kubernetes cluster, and the kubectl command-line tool must be configured to communicate with your cluster. It is recommended to run this tutorial on a cluster with at least two nodes that are not acting as control plane hosts. If you do not already have a cluster, you can create one by using minikube or you can use one of these Kubernetes playgrounds:

Set up Kubernetes tools on your computer.

Before you begin You must use a kubectl version that is within one minor version difference of your cluster. For example, a v1.33 client can communicate with v1.32, v1.33, and v1.34 control planes. Using the latest compatible version of kubectl helps avoid unforeseen issues. Install kubectl on Windows The following methods exist for installing kubectl on Windows: Install kubectl binary on Windows (via direct download or curl) Install on Windows using Chocolatey, Scoop, or winget Install kubectl binary on Windows (via direct download or curl) You have two options for installing kubectl on your Windows device

This page provides an overview of available configuration options and best practices for cluster multi-tenancy. Sharing clusters saves costs and simplifies administration. However, sharing clusters also presents challenges such as security, fairness, and managing noisy neighbors. Clusters can be shared in many ways. In some cases, different applications may run in the same cluster. In other cases, multiple instances of the same application may run in the same cluster, one for each end user.