# Kubernetes v1.36: Advancing Workload-Aware Scheduling

DevFeed: [Kubernetes v1.36: Advancing Workload-Aware Scheduling](<https://devfeed.tech/articles/kubernetes-v1-36-advancing-workload-aware-scheduling-4551.md>)

Original publisher: [Read original article](<https://kubernetes.io/blog/2026/05/13/kubernetes-v1-36-advancing-workload-aware-scheduling/>)

Published: 2026-05-13T18:35:00Z

Content type: release

Language: en

Sources: [Kubernetes Blog](<https://devfeed.tech/sources/kubernetes-blog.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>)

Tags: [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [api](<https://devfeed.tech/tags/api.md>), [batch](<https://devfeed.tech/tags/batch.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [release](<https://devfeed.tech/tags/release.md>), [scalability](<https://devfeed.tech/tags/scalability.md>)

## AI overview

Kubernetes v1.36 updates workload-aware scheduling by separating static Workload templates from runtime PodGroup state. It adds a PodGroup scheduling cycle, early topology-aware scheduling and workload-aware preemption, Dynamic Resource Allocation support, and initial Job controller integration.

## Source excerpt

AI/ML and batch workloads introduce unique scheduling challenges that go beyond simple Pod-by-Pod scheduling. In Kubernetes v1.35, we introduced the first tranche of workload-aware scheduling improvements, featuring the foundational Workload API alongside basic gang scheduling support built on a Pod-based framework, and an opportunistic batching feature to efficiently process identical Pods. Kubernetes v1.36 introduces a significant architectural evolution by cleanly separating API concerns: the Workload API acts as a static template, while the new PodGroup API handles the runtime state. To support this, the kube-scheduler features a new PodGroup scheduling cycle that enables atomic workload processing and paves the way for future enhancements. This release also debuts the first iterations of topology-aware scheduling and workload-aware preemption to advance scheduling capabilities. Additionally, ResourceClaim support for workloads unlocks Dynamic Resource Allocation (DRA) for PodGroups. Finally, to demonstrate real-world readiness, v1.36 delivers the first phase of integration between the Job controller and the new API. Workload and PodGroup API updates The Workload API now serves as a static template, while the new PodGroup API describes the runtime object. Kubernetes v1.36 introduces the Workload and PodGroup APIs as part of the scheduling.k8s.io/v1alpha2 API group, completely replacing the previous v1alpha1 API version. In v1.35, Pod groups and their runtime states were embedded within the Workload resource. The new model decouples these concepts: the Workload now serves as a static template object, while the PodGroup manages the runtime state. This separation also improves performance and scalability as the PodGroup API allows per-replica sharding of status updates. Because the Workload API acts merely as a template, the kube-scheduler's logic is streamlined. The scheduler can directly read the PodGroup, which contains all the information required by the schedu