Dynamic Resource Allocation

Dynamic resource allocation is an API for requesting and sharing resources between pods and containers inside a pod. It is a generalization of the persistent volumes API for generic resources. Third-party resource drivers are responsible for tracking and allocating resources, with additional support provided by Kubernetes via structured parameters (introduced in Kubernetes 1.30). When a driver uses structured parameters, Kubernetes handles scheduling and resource allocation without having to communicate with the driver. Different kinds of resources support arbitrary parameters for defining requirements and initialization.

Before you begin

Kubernetes v1.30 includes cluster-level API support for dynamic resource allocation, but it needs to be enabled explicitly. You also must install a resource driver for specific resources that are meant to be managed using this API. If you are not running Kubernetes v1.30, check the documentation for that version of Kubernetes.

API

The resource.k8s.io/v1alpha2 API group provides these types:

ResourceClass

Defines which resource driver handles a certain kind of resource and provides common parameters for it. ResourceClasses are created by a cluster administrator when installing a resource driver.

ResourceClaim

Defines a particular resource instance that is required by a workload. Created by a user (lifecycle managed manually, can be shared between different Pods) or for individual Pods by the control plane based on a ResourceClaimTemplate (automatic lifecycle, typically used by just one Pod).

ResourceClaimTemplate

Defines the spec and some metadata for creating ResourceClaims. Created by a user when deploying a workload.

PodSchedulingContext

Used internally by the control plane and resource drivers to coordinate pod scheduling when ResourceClaims need to be allocated for a Pod.

ResourceSlice

Used with structured parameters to publish information about resources that are available in the cluster.

ResourceClaimParameters

Contain the parameters for a ResourceClaim which influence scheduling, in a format that is understood by Kubernetes (the "structured parameter model"). Additional parameters may be embedded in an opaque extension, for use by the vendor driver when setting up the underlying resource.

ResourceClassParameters

Similar to ResourceClaimParameters, the ResourceClassParameters provides a type for ResourceClass parameters which is understood by Kubernetes.

Parameters for ResourceClass and ResourceClaim are stored in separate objects, typically using the type defined by a CRD that was created when installing a resource driver.

The developer of a resource driver decides whether they want to handle these parameters in their own external controller or instead rely on Kubernetes to handle them through the use of structured parameters. A custom controller provides more flexibility, but cluster autoscaling is not going to work reliably for node-local resources. Structured parameters enable cluster autoscaling, but might not satisfy all use-cases.

When a driver uses structured parameters, it is still possible to let the end-user specify parameters with vendor-specific CRDs. When doing so, the driver needs to translate those custom parameters into the in-tree types. Alternatively, a driver may also document how to use the in-tree types directly.

The core/v1 PodSpec defines ResourceClaims that are needed for a Pod in a resourceClaims field. Entries in that list reference either a ResourceClaim or a ResourceClaimTemplate. When referencing a ResourceClaim, all Pods using this PodSpec (for example, inside a Deployment or StatefulSet) share the same ResourceClaim instance. When referencing a ResourceClaimTemplate, each Pod gets its own instance.

The resources.claims list for container resources defines whether a container gets access to these resource instances, which makes it possible to share resources between one or more containers.

Here is an example for a fictional resource driver. Two ResourceClaim objects will get created for this Pod and each container gets access to one of them.

apiVersion: resource.k8s.io/v1alpha2
kind: ResourceClass
name: resource.example.com
driverName: resource-driver.example.com
---
apiVersion: cats.resource.example.com/v1
kind: ClaimParameters
name: large-black-cat-claim-parameters
spec:
  color: black
  size: large
---
apiVersion: resource.k8s.io/v1alpha2
kind: ResourceClaimTemplate
metadata:
  name: large-black-cat-claim-template
spec:
  spec:
    resourceClassName: resource.example.com
    parametersRef:
      apiGroup: cats.resource.example.com
      kind: ClaimParameters
      name: large-black-cat-claim-parameters
–--
apiVersion: v1
kind: Pod
metadata:
  name: pod-with-cats
spec:
  containers:
  - name: container0
    image: ubuntu:20.04
    command: ["sleep", "9999"]
    resources:
      claims:
      - name: cat-0
  - name: container1
    image: ubuntu:20.04
    command: ["sleep", "9999"]
    resources:
      claims:
      - name: cat-1
  resourceClaims:
  - name: cat-0
    source:
      resourceClaimTemplateName: large-black-cat-claim-template
  - name: cat-1
    source:
      resourceClaimTemplateName: large-black-cat-claim-template

Scheduling

Without structured parameters

In contrast to native resources (CPU, RAM) and extended resources (managed by a device plugin, advertised by kubelet), without structured parameters the scheduler has no knowledge of what dynamic resources are available in a cluster or how they could be split up to satisfy the requirements of a specific ResourceClaim. Resource drivers are responsible for that. They mark ResourceClaims as "allocated" once resources for it are reserved. This also then tells the scheduler where in the cluster a ResourceClaim is available.

ResourceClaims can get allocated as soon as they are created ("immediate allocation"), without considering which Pods will use them. The default is to delay allocation until a Pod gets scheduled which needs the ResourceClaim (i.e. "wait for first consumer").

In that mode, the scheduler checks all ResourceClaims needed by a Pod and creates a PodScheduling object where it informs the resource drivers responsible for those ResourceClaims about nodes that the scheduler considers suitable for the Pod. The resource drivers respond by excluding nodes that don't have enough of the driver's resources left. Once the scheduler has that information, it selects one node and stores that choice in the PodScheduling object. The resource drivers then allocate their ResourceClaims so that the resources will be available on that node. Once that is complete, the Pod gets scheduled.

As part of this process, ResourceClaims also get reserved for the Pod. Currently ResourceClaims can either be used exclusively by a single Pod or an unlimited number of Pods.

One key feature is that Pods do not get scheduled to a node unless all of their resources are allocated and reserved. This avoids the scenario where a Pod gets scheduled onto one node and then cannot run there, which is bad because such a pending Pod also blocks all other resources like RAM or CPU that were set aside for it.

With structured parameters

When a driver uses structured parameters, the scheduler takes over the responsibility of allocating resources to a ResourceClaim whenever a pod needs them. It does so by retrieving the full list of available resources from ResourceSlice objects, tracking which of those resources have already been allocated to existing ResourceClaims, and then selecting from those resources that remain. The exact resources selected are subject to the constraints provided in any ResourceClaimParameters or ResourceClassParameters associated with the ResourceClaim.

The chosen resource is recorded in the ResourceClaim status together with any vendor-specific parameters, so when a pod is about to start on a node, the resource driver on the node has all the information it needs to prepare the resource.

By using structured parameters, the scheduler is able to reach a decision without communicating with any DRA resource drivers. It is also able to schedule multiple pods quickly by keeping information about ResourceClaim allocations in memory and writing this information to the ResourceClaim objects in the background while concurrently binding the pod to a node.

Monitoring resources

The kubelet provides a gRPC service to enable discovery of dynamic resources of running Pods. For more information on the gRPC endpoints, see the resource allocation reporting.

Pre-scheduled Pods

When you - or another API client - create a Pod with spec.nodeName already set, the scheduler gets bypassed. If some ResourceClaim needed by that Pod does not exist yet, is not allocated or not reserved for the Pod, then the kubelet will fail to run the Pod and re-check periodically because those requirements might still get fulfilled later.

Such a situation can also arise when support for dynamic resource allocation was not enabled in the scheduler at the time when the Pod got scheduled (version skew, configuration, feature gate, etc.). kube-controller-manager detects this and tries to make the Pod runnable by triggering allocation and/or reserving the required ResourceClaims.

It is better to avoid bypassing the scheduler because a Pod that is assigned to a node blocks normal resources (RAM, CPU) that then cannot be used for other Pods while the Pod is stuck. To make a Pod run on a specific node while still going through the normal scheduling flow, create the Pod with a node selector that exactly matches the desired node:

apiVersion: v1
kind: Pod
metadata:
  name: pod-with-cats
spec:
  nodeSelector:
    kubernetes.io/hostname: name-of-the-intended-node
  ...

You may also be able to mutate the incoming Pod, at admission time, to unset the .spec.nodeName field and to use a node selector instead.

Enabling dynamic resource allocation

Dynamic resource allocation is an alpha feature and only enabled when the DynamicResourceAllocation feature gate and the resource.k8s.io/v1alpha2 API group are enabled. For details on that, see the --feature-gates and --runtime-config kube-apiserver parameters. kube-scheduler, kube-controller-manager and kubelet also need the feature gate.

A quick check whether a Kubernetes cluster supports the feature is to list ResourceClass objects with:

kubectl get resourceclasses

If your cluster supports dynamic resource allocation, the response is either a list of ResourceClass objects or:

No resources found

If not supported, this error is printed instead:

error: the server doesn't have a resource type "resourceclasses"

The default configuration of kube-scheduler enables the "DynamicResources" plugin if and only if the feature gate is enabled and when using the v1 configuration API. Custom configurations may have to be modified to include it.

In addition to enabling the feature in the cluster, a resource driver also has to be installed. Please refer to the driver's documentation for details.

What's next

• For more information on the design, see the Dynamic Resource Allocation KEP and the Structured Parameters KEP.