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| --- | ||
| layout: blog | ||
| title: "Kubernetes 1.26: Alpha API For Dynamic Resource Allocation" | ||
| date: 2022-12-15 | ||
| slug: dynamic-resource-allocation | ||
| --- | ||
|
|
||
| **Authors:** Patrick Ohly (Intel), Kevin Klues (NVIDIA) | ||
|
|
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| 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: | ||
|
|
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| - 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. | ||
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| Third-party resource drivers are responsible for interpreting these parameters | ||
| as well as tracking and allocating resources as requests come in. | ||
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| Dynamic resource allocation is an *alpha feature* and only enabled when the | ||
| `DynamicResourceAllocation` [feature | ||
| gate](/docs/reference/command-line-tools-reference/feature-gates/) and the | ||
| `resource.k8s.io/v1alpha1` {{< glossary_tooltip text="API group" | ||
| term_id="api-group" >}} are enabled. For details, see the | ||
| `--feature-gates` and `--runtime-config` [kube-apiserver | ||
| parameters](/docs/reference/command-line-tools-reference/kube-apiserver/). | ||
| The kube-scheduler, kube-controller-manager and kubelet components all need | ||
| the feature gate enabled as well. | ||
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| The default configuration of kube-scheduler enables the `DynamicResources` | ||
| plugin if and only if the feature gate is enabled. Custom configurations may | ||
| have to be modified to include it. | ||
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| 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](#running-the-test-driver) for step-by-step instructions. | ||
|
|
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| ## API | ||
|
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| The new `resource.k8s.io/v1alpha1` {{< glossary_tooltip text="API group" | ||
| term_id="api-group" >}} provides four new types: | ||
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| 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. | ||
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| 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). | ||
|
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| ResourceClaimTemplate | ||
| : Defines the spec and some meta data for creating | ||
| ResourceClaims. Created by a user when deploying a workload. | ||
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| PodScheduling | ||
| : Used internally by the control plane and resource drivers | ||
| to coordinate pod scheduling when ResourceClaims need to be allocated | ||
| for a Pod. | ||
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| Parameters for ResourceClass and ResourceClaim are stored in separate objects, | ||
| typically using the type defined by a {{< glossary_tooltip | ||
| term_id="CustomResourceDefinition" text="CRD" >}} that was created when | ||
| installing a resource driver. | ||
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| 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 | ||
| 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. | ||
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| 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. | ||
|
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| 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. | ||
|
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| Assuming a resource driver called `resource-driver.example.com` was installed | ||
| together with the following resource class: | ||
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| ``` | ||
| 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 type | ||
| `resource.example.com` as follows: | ||
|
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| ```yaml | ||
| --- | ||
| 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 | ||
| ``` | ||
| ## Scheduling | ||
| In contrast to native resources (such as CPU or RAM) and | ||
| [extended resources](/docs/concepts/configuration/manage-resources-containers/#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. | ||
| ## 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 this | ||
| [limitation](https://github.com/kubernetes/kubernetes/issues/114005) in the | ||
| future. | ||
|
|
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| ## 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: | ||
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| - [k8s.io/dynamic-resource-allocation/controller](https://github.com/kubernetes/dynamic-resource-allocation/tree/release-1.26/controller) | ||
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| 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: | ||
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| - [k8s.io/dynamic-resource-allocation/kubeletplugin](https://github.com/kubernetes/dynamic-resource-allocation/tree/release-1.26/kubeletplugin) | ||
|
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| It is up to the driver developer to decide how these two components | ||
| communicate. The [KEP](https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/3063-dynamic-resource-allocation/README.md) outlines an [approach using | ||
| CRDs](https://github.com/kubernetes/enhancements/tree/master/keps/sig-node/3063-dynamic-resource-allocation#implementing-a-plugin-for-node-resources). | ||
|
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| Within SIG Node, we also plan to provide a complete [example | ||
| driver](https://github.com/kubernetes-sigs/dra-example-driver) that can serve | ||
| as a template for other drivers. | ||
|
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| ## Running the test driver | ||
|
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| 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](https://github.com/container-orchestrated-devices/container-device-interface) | ||
| (CDI). For example, you can run CRI-O [v1.23.2](https://github.com/cri-o/cri-o/releases/tag/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. | ||
|
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| First, clone the Kubernetes source code. Inside that directory, run: | ||
|
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||
| ```console | ||
| $ 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. | ||
|
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| ```console | ||
| $ 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: | ||
|
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| ```console | ||
| $ 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. | ||
|
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| Now the cluster is ready to create objects: | ||
|
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| ```console | ||
| $ 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: | ||
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| ```console | ||
| $ kubectl logs test-inline-claim with-resource | grep user_a | ||
| user_a='b' | ||
| ``` | ||
|
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| ## Next steps | ||
|
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| - See the | ||
| [Dynamic Resource Allocation](https://github.com/kubernetes/enhancements/blob/master/keps/sig-node/3063-dynamic-resource-allocation/README.md) | ||
| KEP for more information on the design. | ||
| - Read [Dynamic Resource Allocation](/docs/concepts/scheduling-eviction/dynamic-resource-allocation/) | ||
| in the official Kubernetes documentation. | ||
| - You can participate in | ||
| [SIG Node](https://github.com/kubernetes/community/blob/master/sig-node/README.md) | ||
| and / or the [CNCF Container Orchestrated Device Working Group](https://github.com/cncf/tag-runtime/blob/master/wg/COD.md). | ||
| - You can view or comment on the [project board](https://github.com/orgs/kubernetes/projects/95/views/1) | ||
| 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… |