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Priority-based scheduling

Priority-based scheduling is a policy that selects or orders eligible work for execution according to assigned relative priorities.

How 2 Manning books explain it

Distributed Machine Learning Patterns

Priority scheduling is a scheduling algorithm that orders work according to assigned priority levels and may preempt lower-priority work.

How it is used
Prioritizes shared-cluster jobs so urgent machine-learning work can proceed despite limited compute capacity.
How deep it goes
Defines priority ordering and preemption through queue and running-job scenarios, then addresses governance risks without configuration details.
Examples
Illustrates four jobs ranked high, medium, and low, plus preemption of running low-priority job B by high-priority job A.
What you can do after
Evaluate/understand when priority ordering and preemption unblock urgent work, and when priority limits are needed to protect other jobs.

Read how Distributed Machine Learning Patterns explains priority-based scheduling →

Kubernetes for Developers

Priority is an integer value assigned to a Pod that influences its scheduling order and eviction ranking.

How it is used
Ranks Pods for scheduling and eviction when cluster resources are constrained.
How deep it goes
Defines integer priorities and ranking behavior, then demonstrates PriorityClass and Deployment configuration.
Examples
Uses high-priority YAML, a three-replica timeserver Deployment, and alternative preempting and non-preempting policies.
What you can do after
Configure Pod priority with PriorityClass and priorityClassName, including whether lower-priority Pods may be preempted.

Read how Kubernetes for Developers explains priority-based scheduling →

Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.