Workload API

• 1: Pod Group Policies
• 2: Topology-Aware Workload Scheduling

apiVersion: scheduling.k8s.io/v1alpha1
kind: Workload
metadata:
  name: training-job-workload
  namespace: some-ns
spec:
  controllerRef:
    apiGroup: batch
    kind: Job
    name: training-job
  podGroups:
  - name: workers
    policy:
      gang:
        # The gang is schedulable only if 4 pods can run at once
        minCount: 4

1 - Pod Group Policies

Every pod group defined in a Workload must declare a scheduling policy. This policy dictates how the scheduler treats the collection of Pods.

Policy types

The API currently supports two policy types: basic and gang. You must specify exactly one policy for each group.

Basic policy

The basic policy instructs the scheduler to evaluate all Pods on a best-effort basis. Unlike the gang policy, a PodGroup using the basic policy is considered feasible regardless of how many of its Pods are currently schedulable.

The primary reason to use the basic policy is to organize the Pods within your Workload for better observability and management, while still evaluating them together within a single, atomic PodGroup scheduling cycle.

This policy can be used for groups of a Workload that do not require simultaneous startup but logically belong to the application, or to open the way for future group constraints that do not imply "all-or-nothing" placement.

policy:
  basic: {}

Gang policy

The gang policy enforces "all-or-nothing" scheduling. This is essential for tightly-coupled workloads where partial startup results in deadlocks or wasted resources.

This can be used for Jobs or any other batch process where all workers must run concurrently to make progress.

The gang policy requires a minCount parameter:

policy:
  gang:
    # The number of Pods that must be schedulable simultaneously
    # for the group to be admitted.
    minCount: 4

What's next

• Read about PodGroup scheduling.
• Read about gang scheduling algorithm.

2 - Topology-Aware Workload Scheduling

Topology-Aware Scheduling (TAS) is a feature of the Workload API that optimizes the placement of pods within the cluster.

TAS ensures that all pods within a PodGroup are co-located into a specific topology domain, such as a single server rack or zone. This minimizes inter-pod communication latency and prevents workload fragmentation across the cluster infrastructure.

Topology-aware scheduling with gang scheduling policy

When applied to PodGroups with gang scheduling policy, TAS simulates the potential assignment (placement) of the full group of pods at once. It guarantees that at least the specified minCount pods can fit together into the same topology domain before committing resources. If no feasible placement is found, the entire PodGroup becomes unschedulable.

This is the recommended approach for workloads like distributed AI and ML training that strictly require proximity to minimize inter-pod communication latency.

If new pods are added to the PodGroup where some pods are already scheduled (for example, if pods are recreated), the scheduler will force all new incoming pods to land on the exact same topology domain where the existing pods currently reside. If that specific domain lacks sufficient capacity for the new pods, the pods will remain pending - even if it means that less than minCount pods are scheduled at this point.

Topology-aware scheduling with basic scheduling policy

Using TAS with basic scheduling policy may exhibit inconsistent behavior. The scheduler may only observe a subset of pods when entering the PodGroup scheduling cycle - therefore placement feasibility is only evaluated for the observed pods, rather than the entire PodGroup. To partially mitigate this limitation, you can use scheduling gates to hold off PodGroup scheduling until all pods within the PodGroup are in the scheduling queue.

If no feasible placement is found for the entire PodGroup, only a subset of pods may be scheduled, and they are guaranteed to meet the scheduling constraints.

If new pods are added to the PodGroup where some pods are already scheduled, the scheduler will act the same as in case of gang policy - forcing the new pods into the same domain, unless there is insufficient capacity (in which case the new pods will remain pending).

API configuration: scheduling constraints

Every PodGroup (or PodGroupTemplate) may optionally declare the schedulingConstraints field, which is interpreted by the placement-based PodGroup scheduling algorithm. If constraints are defined in PodGroupTemplate, they will be copied to referencing PodGroups.

As of Kubernetes v1.36, the API supports topology constraints.

Topology constraint

To define a topology constraint for a PodGroup you need to set a key, which corresponds to a Kubernetes node label, representing the target topology domain (for example, a rack or a zone). The scheduler strictly enforces that all pods within the PodGroup are placed onto nodes that share the exact same value for this specified label.

Here is an example of a PodGroup configured with a topology constraint:

apiVersion: scheduling.k8s.io/v1alpha2
kind: PodGroup
metadata:
  name: example-podgroup
spec:
  schedulingPolicy:
    gang:
      minCount: 4
  schedulingConstraints:
    topology:
      - key: topology.example.com/rack

What's next

• Learn about pod group policies.
• Learn about plugins related Topology-aware Scheduling
• Read about gang scheduling algorithm.