In Kubernetes, scheduling refers to making sure that PodsThe smallest and simplest Kubernetes object. A Pod represents a set of running containers on your cluster. are matched to NodesA node is a worker machine in Kubernetes. so that KubeletAn agent that runs on each node in the cluster. It makes sure that containers are running in a pod. can run them.
A scheduler watches for newly created Pods that have no Node assigned. For every Pod that the scheduler discovers, the scheduler becomes responsible for finding the best Node for that Pod to run on. The scheduler reaches this placement decision taking into account the scheduling principles described below.
If you want to understand why Pods are placed onto a particular Node, or if you’re planning to implement a custom scheduler yourself, this page will help you learn about scheduling.
kube-scheduler is the default scheduler for Kubernetes and runs as part of the control planeThe container orchestration layer that exposes the API and interfaces to define, deploy, and manage the lifecycle of containers. . kube-scheduler is designed so that, if you want and need to, you can write your own scheduling component and use that instead.
For every newly created pod or other unscheduled pods, kube-scheduler selects an optimal node for them to run on. However, every container in pods has different requirements for resources and every pod also has different requirements. Therefore, existing nodes need to be filtered according to the specific scheduling requirements.
In a cluster, Nodes that meet the scheduling requirements for a Pod are called feasible nodes. If none of the nodes are suitable, the pod remains unscheduled until the scheduler is able to place it.
The scheduler finds feasible Nodes for a Pod and then runs a set of functions to score the feasible Nodes and picks a Node with the highest score among the feasible ones to run the Pod. The scheduler then notifies the API server about this decision in a process called binding.
Factors that need taken into account for scheduling decisions include individual and collective resource requirements, hardware / software / policy constraints, affinity and anti-affinity specifications, data locality, inter-workload interference, and so on.
Node selection in kube-scheduler
kube-scheduler selects a node for the pod in a 2-step operation:
The filtering step finds the set of Nodes where it’s feasible to schedule the Pod. For example, the PodFitsResources filter checks whether a candidate Node has enough available resource to meet a Pod’s specific resource requests. After this step, the node list contains any suitable Nodes; often, there will be more than one. If the list is empty, that Pod isn’t (yet) schedulable.
In the scoring step, the scheduler ranks the remaining nodes to choose the most suitable Pod placement. The scheduler assigns a score to each Node that survived filtering, basing this score on the active scoring rules.
Finally, kube-scheduler assigns the Pod to the Node with the highest ranking. If there is more than one node with equal scores, kube-scheduler selects one of these at random.
There are two supported ways to configure the filtering and scoring behavior of the scheduler:
- Scheduling Policies allow you to configure Predicates for filtering and Priorities for scoring.
- Scheduling Profiles allow you to
configure Plugins that implement different scheduling stages, including:
Permit, and others. You can also configure the kube-scheduler to run different profiles.
- Read about scheduler performance tuning
- Read about Pod topology spread constraints
- Read the reference documentation for kube-scheduler
- Learn about configuring multiple schedulers
- Learn about topology management policies
- Learn about Pod Overhead
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