How does the Kubernetes scheduler decide which node to place a Pod on (filtering and scoring phases)?

Think of the Kubernetes scheduler like a hiring manager filling a position. First, you eliminate candidates who do not meet the minimum qualifications (no relevant degree, wrong location, missing certifications) -- that is the filtering phase. Then, among the qualified candidates, you rank them by how well they fit the role (years of experience, cultural fit, salary expectations) -- that is the scoring phase. The best-ranked candidate gets the offer, and in Kubernetes, the best-ranked node gets the Pod.

In Kubernetes, the kube-scheduler is a control-plane component that watches the API server for newly created Pods that have no node assignment (spec.nodeName is empty). When it detects an unscheduled Pod, it begins the scheduling cycle. The scheduler maintains an internal scheduling queue that prioritizes Pods based on their priority class, creation timestamp, and other factors. The entire process happens in two distinct phases: filtering (also called predicates) and scoring (also called priorities).

During the filtering phase, the scheduler evaluates each node against a set of filter plugins. These include PodFitsResources (checking CPU and memory requests against allocatable capacity), PodFitsHostPorts (ensuring requested host ports are available), NodeAffinity (matching node labels against affinity rules), TaintToleration (verifying the Pod tolerates all node taints), PodTopologySpread (enforcing topology spread constraints), and VolumeBinding (checking that required persistent volumes can be provisioned or are available on that node). Any node that fails even one filter is eliminated. If no nodes pass filtering, the Pod remains Pending and the scheduler may trigger preemption if the Pod has sufficient priority to evict lower-priority Pods.

In the scoring phase, each surviving node is evaluated by scoring plugins that assign a value typically between 0 and 100. The NodeResourcesBalancedAllocation plugin favors nodes that would have balanced CPU and memory use after placing the Pod. The ImageLocality plugin gives higher scores to nodes that already have the container image cached, reducing pull time. InterPodAffinity scores nodes based on whether co-locating the Pod with other Pods matches affinity or anti-affinity preferences. The LeastAllocated strategy prefers nodes with the most free resources, while MostAllocated does the opposite for bin-packing. Each plugin score is multiplied by a configurable weight, and the weighted scores are summed. The node with the highest total score is selected, and the scheduler creates a Binding object to assign the Pod to that node.

At production scale with thousands of nodes, the scheduler uses a percentageOfNodesToScore parameter (defaulting to a formula based on cluster size) to avoid evaluating every single node, which would be too slow. For a 5000-node cluster, it might only score 10% of feasible nodes once it has found enough candidates. The scheduler also supports scheduling profiles, allowing you to run multiple schedulers or customize the plugin chain. The scheduling framework has extension points like PreFilter, Filter, PreScore, Score, Reserve, Permit, PreBind, Bind, and PostBind, making it highly extensible.

A non-obvious gotcha is that the scheduler makes decisions based on a snapshot of the cluster state, which can become stale in highly dynamic environments. If two Pods are being scheduled simultaneously and both target the same node, the second Pod may fail to bind because resources were consumed by the first. Additionally, the percentageOfNodesToScore optimization means the scheduler might not always find the globally optimal node -- it finds a good-enough node quickly. Resource requests (not limits) drive scheduling decisions, so Pods without requests are treated as requesting zero resources, which can lead to node overcommitment. Finally, DaemonSet Pods are not scheduled by the default scheduler since Kubernetes 1.12; the DaemonSet controller handles their node assignment directly.