kubernetesschedulingPractitioner

Mastering Workload-Aware Scheduling in Kubernetes v1.36

5 min read Kubernetes BlogMay 13, 2026Reviewed for accuracy

Practitioner — Hands-on experience recommended

Kubernetes v1.36 enhances scheduling capabilities with the introduction of the Workload API and the PodGroup API. These features address critical issues in workload management, particularly around resource allocation and deployment consistency. The Workload API acts as a static template, while the PodGroup API defines runtime objects, allowing for more granular control over how pods are scheduled. This is essential for preventing partial deployments that can lead to resource wastage and potential deadlocks.

At the core of this advancement is the PodGroup scheduling cycle, which ensures that the kube-scheduler takes a single snapshot of the cluster state. This prevents race conditions and guarantees consistency when evaluating the entire group. The scheduler uses a dedicated algorithm that filters and scores potential node placements for all pods in the group. The scheduling decision is then applied atomically, meaning that either all pods in the group are scheduled together, or none are. You can configure parameters like minCount, which specifies the minimum number of pods that must be schedulable at once, to ensure your applications meet their resource needs efficiently.

In production, you need to be aware of several limitations. If new pods are added to a PodGroup after others are scheduled, the existing pods will not be unassigned or evicted, even if the group fails to meet its requirements later. Additionally, for heterogeneous Pod groups or those with inter-Pod dependencies, finding valid placements is not guaranteed. This can lead to challenges in complex deployments, so thorough testing and validation are crucial before rolling out these features in a live environment.

Key takeaways

→Utilize the Workload API as a static template for better workload management.
→Implement the PodGroup API to define runtime objects for your applications.
→Leverage gang scheduling to prevent partial deployments and resource wastage.
→Set the `minCount` parameter to ensure a minimum number of pods are schedulable together.
→Be cautious with heterogeneous Pod groups and inter-Pod dependencies, as valid placements are not guaranteed.

Why it matters

These advancements in Kubernetes scheduling can significantly enhance application reliability and resource efficiency, especially in complex environments where workload management is critical.

Code examples

YAML

1apiVersion: scheduling.k8s.io/v1alpha2
2kind: Workload
3metadata:
4  name: training-job-workload
5  namespace: some-ns
6spec:
7  podGroupTemplates:
8  - name: workers
9    schedulingPolicy:
10      gang:
11        minCount: 4

YAML

1apiVersion: scheduling.k8s.io/v1alpha2
2kind: PodGroup
3metadata:
4  name: training-job-workers-pg
5  namespace: some-ns
6spec:
7  podGroupTemplateRef:
8    workload:
9      workloadName: training-job-workload
10      podGroupTemplateName: workers
11  schedulingPolicy:
12    gang:
13      minCount: 4
14status:
15  conditions:
16  - type: PodGroupScheduled
17    status: "True"
18    lastTransitionTime: 2026-04-03T00:00:00Z

YAML

1apiVersion: scheduling.k8s.io/v1alpha2
2kind: PodGroup
3metadata:
4  name: topology-aware-workers-pg
5spec:
6  schedulingPolicy:
7    gang:
8      minCount: 4
9  schedulingConstraints:
10    topology:
11    - key: topology.kubernetes.io/rack