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+---
+title: Workload Aware Scheduling
+content_type: concept
+weight: 120
+---
+
+This page provides an overview of Workload Aware Scheduling (WAS), a Kubernetes feature
+that enables the kube-scheduler to manage groups of related Pods as a single unit.
+
+## What is Workload Aware Scheduling?
+
+The default Kubernetes scheduler makes decisions for one Pod at a time. This model works sufficiently good for stateless applications,
+but can be inefficient for tightly-coupled workloads like those found in machine learning, scientific computing, or big data analytics.
+These applications often require that a group of Pods must run concurrently to make any progress.
+
+When the scheduler places these Pods individually, it can lead to resource deadlocks or placement inefficiencies.
+For example, half of a job's Pods might be scheduled, consuming cluster resources, while the other half remains pending
+because no single node has enough capacity for them. The job cannot run,
+but the scheduled Pods waste expensive resources that other applications could use.
+
+Workload Aware Scheduling introduces a mechanism for the scheduler to identify and manage a group of Pods as a single, atomic workload.
+This allows for more intelligent placement decisions and is the foundation for features like gang scheduling.
+
+The `Workload` API is used to express these group scheduling requirements.
+
+## Workload API
+
+{{< feature-state feature_gate_name="GenericWorkload" >}}
+
+The `Workload` API resource, available from the `scheduling.k8s.io/v1alpha1` API group, allows you to logically group a set of Pods.
+You then link Pods to a `Workload` to inform the scheduler that they should be considered together.
+Controllers for high-level resources, such as the Job controller, can create `Workload` objects to communicate placement requirements to the scheduler.
+
+A `Workload` resource defines one or more `PodGroup`s. Each `PodGroup` specifies a set of Pods and the scheduling policy that applies to them.
+
+Here is an example manifest for a `Workload` that defines a gang of three Pods:
+
+```yaml
+apiVersion: scheduling.k8s.io/v1alpha1
+kind: Workload
+metadata:
+  name: training-job-workload
+spec:
+  # controllerRef provides a link to the object that manages this Workload,
+  # such as a Kubernetes Job. This is for tooling and observability.
+  controllerRef:
+    apiGroup: batch
+    kind: Job
+    name: training-job
+
+  podGroups:
+  - name: workers
+    policy:
+      gang:
+        # The minimum number of Pods from this group that must be schedulable
+        # at the same time for any of them to be scheduled.
+        minCount: 3
+```
+
+To associate a Pod with this `Workload`, you add a `spec.workloadRef` field to the Pod's manifest.
+This field creates a link to a specific `PodGroup` within the `Workload`.
+
+```yaml
+apiVersion: v1
+kind: Pod
+metadata:
+  name: training-pod-1
+spec:
+  # This reference links the Pod to the 'workers' PodGroup
+  # inside the 'training-job-workload' Workload.
+  workloadRef:
+    name: training-job-workload
+    podGroup: workers
+  # ...
+```
+
+### Pod Group Replicas
+
+For more complex scenarios, you can partition a single `PodGroup` into replicated, independent gangs.
+You achieve this using the `podGroupReplicaKey` field within a Pod's `workloadRef`. This key acts as a label
+to create logical subgroups. The scheduling algorithm is then applied to each subgroup separately.
+
+For example, if you have a `PodGroup` with `minCount: 2` and you create four Pods: two with `podGroupReplicaKey: "0"`
+and two with `podGroupReplicaKey: "1"`, the scheduler will treat them as two independent gangs of two Pods.
+
+```yaml
+apiVersion: v1
+kind: Pod
+metadata:
+  name: training-pod-1
+spec:
+  workloadRef:
+    name: training-job-workload
+    podGroup: workers
+    # All workers with the replica key "0" will be scheduled together as one gang.
+    podGroupReplicaKey: "0"
+  # ...
+```
+
+## Scheduling Policies
+
+For each `PodGroup`, you must specify exactly one scheduling policy. The two available policies are `basic` and `gang`.
+
+### Basic Scheduling
+
+The `basic` policy instructs the scheduler to treat all Pods in a `PodGroup` as independent entities,
+scheduling them using the standard Kubernetes behavior.
+
+Currently, the main reason to use the `basic` policy is to organize the Pods within your `Workload`
+for better observability and management.
+
+While this policy does not add any special group-level scheduling constraints today,
+it provides a foundation for future enhancements. For example, future versions of Kubernetes
+might introduce group-level constraints that apply to a `PodGroup` without requiring
+the all-or-nothing semantics of gang scheduling.
+
+### Gang Scheduling
+
+{{< feature-state feature_gate_name="GangScheduling" >}}
+
+The `gang` policy enforces gang scheduling placement, which ensures a group of Pods are scheduled on an "all-or-nothing" basis.
+Without gang scheduling, a job might be partially scheduled, leading to resource wastage and potential deadlocks.
+
+The `GangScheduling` plugin uses the `Workload` API to implement its logic.
+When you create Pods that are part of a gang-scheduled `PodGroup`, the scheduler follows this process,
+independently per each `PodGroup` and its replica key:
+
+1. The scheduler waits on `PreEnqueue` until the number of Pods that have been created for the specific `PodGroup`
+   is at least equal to the `minCount`. Pods do not enter the active scheduling queue until this condition is met.
+
+2. Once the quorum is met, the scheduler attempts to find node placements for all Pods in the group by taking them pod-by-pod.
+   All assigned Pods wait at a `WaitOnPermit` gate during this process. Future versions will introduce a new,
+   single cycle scheduling phase to find the placement for the entire group at once.
+
+3. If the scheduler finds valid placements for at least `minCount` Pods, it allows all of them to be bound to their assigned nodes.
+   If it cannot find placements for the entire group within a fixed, 5 minutes timeout, none of the Pods are scheduled.
+   Instead, they are moved to the unschedulable queue to wait for cluster resources to free up.
+
+## Enabling the Features
+
+To use the Workload API in your cluster, you must enable the `GenericWorkload` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/)
+on both the `kube-apiserver` and `kube-scheduler`.
+
+To use Gang Scheduling, you must also enable the `GangScheduling` [feature gate](/docs/reference/command-line-tools-reference/feature-gates/)
+on the `kube-scheduler`. When this feature gate is enabled, the `GangScheduling` plugin is enabled by default in the scheduler's profiles.
diff --git a/content/en/docs/reference/command-line-tools-reference/feature-gates/GangScheduling.md b/content/en/docs/reference/command-line-tools-reference/feature-gates/GangScheduling.md
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+---
+title: GangScheduling
+content_type: feature_gate
+_build:
+  list: never
+  render: false
+
+stages:
+  - stage: alpha
+    defaultValue: false
+    fromVersion: "1.35"
+---
+
+Enables the GangScheduling plugin in kube-scheduler, which implements "all-or-nothing"
+scheduling algorithm. The Workload API is used to express the requirements.
diff --git a/content/en/docs/reference/command-line-tools-reference/feature-gates/GenericWorkload.md b/content/en/docs/reference/command-line-tools-reference/feature-gates/GenericWorkload.md
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+---
+title: GenericWorkload
+content_type: feature_gate
+_build:
+  list: never
+  render: false
+
+stages:
+  - stage: alpha
+    defaultValue: false
+    fromVersion: "1.35"
+---
+
+Enables the scheduling.k8s.io/v1alpha1 Workload API to express scheduling requirements
+at the workload level. Pods can now reference a specific Workload PodGroup using the spec.workloadRef field.
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