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 the
feature gate and the
API group are enabled. For details,
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
plugin 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. See below for step-by-step instructions.
resource.k8s.io/v1alpha1 API group
provides four new types:
- 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.
- 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).
- Defines the spec and some meta data for creating ResourceClaims. Created by a user when deploying a workload.
- 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 new
resourceClaims field. Entries in that list reference either a ResourceClaim
or a ResourceClaimTemplate. When referencing a ResourceClaim, all Pods using
.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
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
An end-user could then allocate two specific resources of type
resource.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
In contrast to native resources (such as CPU or RAM) and extended 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.
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 this
limitation in the
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
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:
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 the Container 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
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
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
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
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
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'
- See the Dynamic Resource Allocation KEP for more information on the design.
- Read Dynamic Resource Allocation in the official Kubernetes documentation.
- You can participate in SIG Node and / or the CNCF Container Orchestrated Device Working Group.
- You can view or comment on the project board for dynamic resource allocation.
- In order to move this feature towards beta, we need feedback from hardware vendors, so here's a call to action: try out this feature, consider how it can help with problems that your users are having, and write resource drivers…