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Posted on • Originally published at kubernetes.io on

Blog: Kubernetes 1.26: Alpha API For Dynamic Resource Allocation

Authors: Patrick Ohly (Intel), Kevin Klues (NVIDIA)

Dynamic resource allocation is a new API for requesting resources. It is a generalization of the persistent volumes API for generic resources, making it possible to:

  • access the same resource instance in different pods and containers,
  • attach arbitrary constraints to a resource request to get the exact resource you are looking for,
  • initialize a resource according to parameters provided by the user.

Third-party resource drivers are responsible for interpreting these parameters as well as tracking and allocating resources as requests come in.

Dynamic resource allocation is an alpha feature and only enabled when theDynamicResourceAllocation feature gate and theresource.k8s.io/v1alpha1 API group are enabled. For details, see the--feature-gates and --runtime-config kube-apiserver parameters. The kube-scheduler, kube-controller-manager and kubelet components all need the feature gate enabled as well.

The default configuration of kube-scheduler enables the DynamicResourcesplugin if and only if the feature gate is enabled. Custom configurations may have to be modified to include it.

Once dynamic resource allocation is enabled, resource drivers can be installed to manage certain kinds of hardware. Kubernetes has a test driver that is used for end-to-end testing, but also can be run manually. Seebelow for step-by-step instructions.

API

The new resource.k8s.io/v1alpha1 API group provides four new 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 instances 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 meta data for creating ResourceClaims. Created by a user when deploying a workload.
PodScheduling
Used internally by the control plane and resource drivers to coordinate pod scheduling when ResourceClaims need to be allocated for a Pod.

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.

With this alpha feature enabled, the spec of Pod defines ResourceClaims that are needed for a Pod to run: this information goes into a newresourceClaims field. Entries in that list reference either a ResourceClaim or a ResourceClaimTemplate. When referencing a ResourceClaim, all Pods using this .spec (for example, inside a Deployment or StatefulSet) share the same ResourceClaim instance. When referencing a ResourceClaimTemplate, each Pod gets its own ResourceClaim instance.

For a container defined within a Pod, the resources.claims list defines whether that container gets access to these resource instances, which makes it possible to share resources between one or more containers inside the same Pod. For example, an init container could set up the resource before the application uses it.

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

Assuming a resource driver called resource-driver.example.com was installed together with the following resource class:

apiVersion: resource.k8s.io/v1alpha1
kind: ResourceClass
name: resource.example.com
driverName: resource-driver.example.com

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An end-user could then allocate two specific resources of typeresource.example.com as follows:

---
apiVersion: cats.resource.example.com/v1
kind: ClaimParameters
name: large-black-cats
spec:
 color: black
 size: large
---
apiVersion: resource.k8s.io/v1alpha1
kind: ResourceClaimTemplate
metadata:
 name: large-black-cats
spec:
 spec:
 resourceClassName: resource.example.com
 parametersRef:
 apiGroup: cats.resource.example.com
 kind: ClaimParameters
 name: large-black-cats
–--
apiVersion: v1
kind: Pod
metadata:
 name: pod-with-cats
spec:
 containers: # two example containers; each container claims one cat resource
 - name: first-example
 image: ubuntu:22.04
 command: ["sleep", "9999"]
 resources:
 claims:
 - name: cat-0
 - name: second-example
 image: ubuntu:22.04
 command: ["sleep", "9999"]
 resources:
 claims:
 - name: cat-1
 resourceClaims:
 - name: cat-0
 source:
 resourceClaimTemplateName: large-black-cats
 - name: cat-1
 source:
 resourceClaimTemplateName: large-black-cats

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Scheduling

In contrast to native resources (such as CPU or RAM) andextended resources(managed by a device plugin, advertised by kubelet), 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. Drivers mark ResourceClaims as allocated once resources for it are reserved. This also then tells the scheduler where in the cluster a claimed resource is actually available.

ResourceClaims can get resources allocated as soon as the ResourceClaim is created (immediate allocation), without considering which Pods will use the resource. The default (wait for first consumer) is to delay allocation until a Pod that relies on the ResourceClaim becomes eligible for scheduling. This design with two allocation options is similar to how Kubernetes handles storage provisioning with PersistentVolumes and PersistentVolumeClaims.

In the wait for first consumer mode, the scheduler checks all ResourceClaims needed by a Pod. If the Pods has any ResourceClaims, the scheduler creates a PodScheduling (a special object that requests scheduling details on behalf of the Pod). The PodScheduling has the same name and namespace as the Pod and the Pod as its as owner. Using its PodScheduling, the scheduler 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 resource information, it selects one node and stores that choice in the PodScheduling object. The resource drivers then allocate resources based on the relevant ResourceClaims so that the resources will be available on that selected node. Once that resource allocation is complete, the scheduler attempts to schedule the Pod to a suitable node. Scheduling can still fail at this point; for example, a different Pod could be scheduled to the same node in the meantime. If this happens, already allocated ResourceClaims may get deallocated to enable scheduling onto a different node.

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.

Limitations

The scheduler plugin must be involved in scheduling Pods which use ResourceClaims. Bypassing the scheduler by setting the nodeName field leads to Pods that the kubelet refuses to start because the ResourceClaims are not reserved or not even allocated. It may be possible to remove thislimitation in the future.

Writing a resource driver

A dynamic resource allocation driver typically consists of two separate-but-coordinating components: a centralized controller, and a DaemonSet of node-local kubelet plugins. Most of the work required by the centralized controller to coordinate with the scheduler can be handled by boilerplate code. Only the business logic required to actually allocate ResourceClaims against the ResourceClasses owned by the plugin needs to be customized. As such, Kubernetes provides the following package, including APIs for invoking this boilerplate code as well as a Driver interface that you can implement to provide their custom business logic:

Likewise, boilerplate code can be used to register the node-local plugin with the kubelet, as well as start a gRPC server to implement the kubelet plugin API. For drivers written in Go, the following package is recommended:

It is up to the driver developer to decide how these two components communicate. The KEP outlines an approach using CRDs.

Within SIG Node, we also plan to provide a complete example driver that can serve as a template for other drivers.

Running the test driver

The following steps bring up a local, one-node cluster directly from the Kubernetes source code. As a prerequisite, your cluster must have nodes with a container runtime that supports theContainer Device Interface(CDI). For example, you can run CRI-O v1.23.2 or later. Once containerd v1.7.0 is released, we expect that you can run that or any later version. In the example below, we use CRI-O.

First, clone the Kubernetes source code. Inside that directory, run:

$ hack/install-etcd.sh
...

$ RUNTIME_CONFIG=resource.k8s.io/v1alpha1 \
 FEATURE_GATES=DynamicResourceAllocation=true \
 DNS_ADDON="coredns" \
 CGROUP_DRIVER=systemd \
 CONTAINER_RUNTIME_ENDPOINT=unix:///var/run/crio/crio.sock \
 LOG_LEVEL=6 \
 ENABLE_CSI_SNAPSHOTTER=false \
 API_SECURE_PORT=6444 \
 ALLOW_PRIVILEGED=1 \
 PATH=$(pwd)/third_party/etcd:$PATH \
 ./hack/local-up-cluster.sh -O
...
To start using your cluster, you can open up another terminal/tab and run:

 export KUBECONFIG=/var/run/kubernetes/admin.kubeconfig
...

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Once the cluster is up, in another terminal run the test driver controller. KUBECONFIG must be set for all of the following commands.

$ go run ./test/e2e/dra/test-driver --feature-gates ContextualLogging=true -v=5 controller

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In another terminal, run the kubelet plugin:

$ sudo mkdir -p /var/run/cdi && \
 sudo chmod a+rwx /var/run/cdi /var/lib/kubelet/plugins_registry /var/lib/kubelet/plugins/
$ go run ./test/e2e/dra/test-driver --feature-gates ContextualLogging=true -v=6 kubelet-plugin

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Changing the permissions of the directories makes it possible to run and (when using delve) debug the kubelet plugin as a normal user, which is convenient because it uses the already populated Go cache. Remember to restore permissions with sudo chmod go-w when done. Alternatively, you can also build the binary and run that as root.

Now the cluster is ready to create objects:

$ kubectl create -f test/e2e/dra/test-driver/deploy/example/resourceclass.yaml
resourceclass.resource.k8s.io/example created

$ kubectl create -f test/e2e/dra/test-driver/deploy/example/pod-inline.yaml
configmap/test-inline-claim-parameters created
resourceclaimtemplate.resource.k8s.io/test-inline-claim-template created
pod/test-inline-claim created

$ kubectl get resourceclaims
NAME RESOURCECLASSNAME ALLOCATIONMODE STATE AGE
test-inline-claim-resource example WaitForFirstConsumer allocated,reserved 8s

$ kubectl get pods
NAME READY STATUS RESTARTS AGE
test-inline-claim 0/2 Completed 0 21s

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The test driver doesn't do much, it only sets environment variables as defined in the ConfigMap. The test pod dumps the environment, so the log can be checked to verify that everything worked:

$ kubectl logs test-inline-claim with-resource | grep user_a
user_a='b'

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Next steps

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