CRI Resource Manager for Kubernetes¶

Introduction¶

CRI Resource Manager is a Container Runtime Interface Proxy. It sits between clients and the actual Container Runtime implementation (containerd, cri-o, dockershim+docker), relaying requests and responses back and forth. The main purpose of the proxy is to apply hardware-aware resource allocation policies to the containers running in the system.

Policies are applied by either modifying a request before forwarding it, or by performing extra actions related to the request during its processing and proxying. There are several policies available, each with a different set of goals in mind and implementing different hardware allocation strategies. The details of whether and how a CRI request is altered or if extra actions are performed depend on which policy is active in CRI Resource Manager, and how that policy is configured.

The current goal for the CRI Resource Manager is to prototype and experiment with new Kubernetes container placement policies. The existing policies are written with this in mind and the intended setup is for the Resource Manager to only act as a proxy for the Kubernetes Node Agent, kubelet.

TL;DR Setup¶

If you want to give CRI Resource Manager a try, here is the list of things you need to do, assuming you already have a Kubernetes cluster up and running, using either containerd or cri-o as the runtime.

Set up kubelet to use CRI Resource Manager as the runtime.
Set up CRI Resource Manager to use the runtime with a policy.

For kubelet you do this by altering its command line options like this:

   kubelet <other-kubelet-options> --container-runtime=remote \
     --container-runtime-endpoint=unix:///var/run/cri-resmgr/cri-resmgr.sock

For CRI Resource Manager, you need to provide a configuration file, and also a socket path if you don’t use containerd or you run it with a different socket path.

   # for containerd with default socket path
   cri-resmgr --force-config <config-file> --runtime-socket unix:///var/run/containerd/containerd.sock
   # for cri-o
   cri-resmgr --force-config <config-file> --runtime-socket unix:///var/run/crio/crio.sock

The choice of policy to use along with any potential parameters specific to that policy are taken from the configuration file. You can take a look at the sample configurations for some minimal/trivial examples. For instance, you can use sample-configs/memtier-policy.cfg as <config-file> to activate the topology aware policy with memory tiering support.

NOTE: Currently the available policies are work in progress.

Setting Up kubelet To Use CRI Resource Manager as the Runtime¶

To let CRI Resource Manager act as a proxy between kubelet and the CRI runtime you need to configure kubelet to connect to CRI Resource Manager instead of the runtime. You do this by passing extra command line options to kubelet like this:

   kubelet <other-kubelet-options> --container-runtime=remote \
     --container-runtime-endpoint=unix:///var/run/cri-resmgr/cri-resmgr.sock

Setting Up CRI Resource Manager¶

Setting up CRI Resource Manager involves pointing it to your runtime and providing it with a configuration. Pointing to the runtime is done using the --runtime-socket <path> and, optionally, the --image-socket <path>.

For providing a configuration there are two options:

use a local configuration YAML file
use the CRI Resource Manager Agent and a ConfigMap

The former is easier to set up and it is also the preferred way to run CRI Resource Manager for development, and in some cases testing. Setting up the latter is a bit more involved but it allows you to

manage policy configuration for your cluster as a single source, and
dynamically update that configuration

Using a Local Configuration From a File¶

This is the easiest way to run CRI Resource Manager for development or testing. You can do it with the following command:

   cri-resmgr --force-config <config-file> --runtime-socket <path>

When started this way CRI Resource Manager reads its configuration from the given file. It also disables its agent interface for external configuration and updates.

Using CRI Resource Manager Agent and a ConfigMap¶

This setup requires an extra component, the CRI Resource Manager Node Agent, to monitor and fetch configuration from the ConfigMap and pass it on to CRI Resource Manager. By default CRI Resource Manager will automatically try to use the agent to acquire configuration, unless you override this by forcing a static local configuration using the --force-config <config-file> option. When using the agent, it is also possible to provide an initial fallback for configuration using the --fallback-config <config-file>. This file will be use before the very first configuration is successfully acquired from the agent.

See the later chapter about how to set up and configure the agent.

CRI Resource Manager Mutating Webhook¶

By default CRI Resource Manager does not see the original container resource requirements specified in the Pod Spec. It tries to calculate these for cpu and memory compute resources using the related parameters present in the CRI container creation request. The resulting estimates are normally accurate for cpu, and also for memory limits. However, it is not possible to use these parameters to estimate memory requests or any extended resources.

If you want to make sure that CRI Resource Manager uses the origin Pod Spec resource requirements, you need to duplicate these as annotations on the Pod. This is necessary if you plan using or writing a policy which needs extended resources.

This process can be fully automated using the CRI Resource Manager Annotating Webhook. Once you built the docker image for it using the [provided Dockerfile][cmd/cri-resmgr-webhook/Dockerfile] and published it, you can set up the webhook with these commands:

  kubectl apply -f cmd/cri-resmgr-webhook/mutating-webhook-config.yaml
  kubectl apply -f cmd/cri-resmgr-webhook/webhook-deployment.yaml

CRI Resource Manager Node Agent¶

CRI Resource Manager can be configured dynamically using the CRI Resource Manager Node Agent and Kubernetes ConfigMaps. The agent can be build using the provided Dockerfile. It can be deployed as a DaemonSet in the cluster using the provided deployment file.

To run the agent manually or as a systemd service, set the environment variable NODE_NAME to the name of the cluster node the agent is running on. If necessary pass it the credentials for accessing the cluster using the the -kubeconfig <file> command line option.

The agent monitors two ConfigMaps for the node, a primary node-specific one, and a secondary group-specific or default one, depending on whether the node belongs to a configuration group. The node-specific ConfigMap always takes precedence over the others.

The names of these ConfigMaps are

cri-resmgr-config.node.$NODE_NAME: primary, node-specific configuration
cri-resmgr-config.group.$GROUP_NAME: secondary group-specific node configuration
cri-resmgr-config.default: secondary: secondary default node configuration

You can assign a node to a configuration group by setting the cri-resource-manager.intel.com/group label on the node to the name of the configuration group. You can remove a node from its group by deleting the node group label.

There is a sample ConfigMap spec that contains anode-specific, a group-specific, and a default ConfigMap examples. See any available policy-specific documentation for more information on the policy configurations.

Using CRI Resource Manager as a Message Dumper¶

You can use CRI Resource Manager to simply inspect all proxied CRI requests and responses without applying any policy. Run CRI Resource Manager with the provided sample configuration for doing this.

Using Docker as the Runtime¶

If you must use docker as the runtime then the proxying setup is slightly more complex. Docker does not natively support the CRI API. Normally kubelet runs an internal protocol translator, dockershim to translate between CRI and the native docker API. To let CRI Resource Manager effectively proxy between kubelet and docker it needs to actually proxy between kubelet and dockershim. For this to be possible, you need to run two instances of kubelet:

real instance talking to CRI Resource Manager/CRI
dockershim instance, acting as a CRI-docker protocol translator

The real kubelet instance you run as you would normally with any other real CRI runtime, but you specify the dockershim socket for the CRI Image Service:

   kubelet <other-kubelet-options> --container-runtime=remote \
     --container-runtime-endpoint=unix:///var/run/cri-resmgr/cri-resmgr.sock \
     --image-service-endpoint=unix:///var/run/dockershim.sock

The dockershim instance you run like this, picking the cgroupfs driver according to your real kubelet instance’s configuration:

  kubelet --experimental-dockershim --port 11250 --cgroup-driver {systemd|cgroupfs}

Logging and Debugging¶

You can control logging and debugging with the --logger-* command line options. By default debug logs are globally disabled. You can turn on full debug logs with the --logger-debug '*' command line option.