multiple-schedulers.md

PLEASE NOTE: This document applies to the HEAD of the source tree

If you are using a released version of Kubernetes, you should refer to the docs that go with that version.

Documentation for other releases can be found at releases.k8s.io.

Multi-Scheduler in Kubernetes

Status: Design & Implementation in progress.

Contact @HaiyangDING for questions & suggestions.

Motivation

In current Kubernetes design, there is only one default scheduler in a Kubernetes cluster. However it is common that multiple types of workload, such as traditional batch, DAG batch, streaming and user-facing production services, are running in the same cluster and they need to be scheduled in different ways. For example, in Omega batch workload and service workload are scheduled by two types of schedulers: the batch workload is scheduled by a scheduler which looks at the current usage of the cluster to improve the resource usage rate and the service workload is scheduled by another one which considers the reserved resources in the cluster and many other constraints since their performance must meet some higher SLOs. Mesos has done a great work to support multiple schedulers by building a two-level scheduling structure. This proposal describes how Kubernetes is going to support multi-scheduler so that users could be able to run their user-provided scheduler(s) to enable some customized scheduling behavior as they need. As previously discussed in #11793, #9920 and #11470, the design of the multiple scheduler should be generic and includes adding a scheduler name annotation to separate the pods. It is worth mentioning that the proposal does not address the question of how the scheduler name annotation gets set although it is reasonable to anticipate that it would be set by a component like admission controller/initializer, as the doc currently does.

Before going to the details of this proposal, below lists a number of the methods to extend the scheduler:

• Write your own scheduler and run it along with Kubernetes native scheduler. This is going to be detailed in this proposal
• Use the callout approach such as the one implemented in #13580
• Recompile the scheduler with a new policy
• Restart the scheduler with a new scheduler policy config file
• Or maybe in future dynamically link a new policy into the running scheduler

Challenges in multiple schedulers

• Separating the pods

  Each pod should be scheduled by only one scheduler. As for implementation, a pod should have an additional field to tell by which scheduler it wants to be scheduled. Besides, each scheduler, including the default one, should have a unique logic of how to add unscheduled pods to its to-be-scheduled pod queue. Details will be explained in later sections.

• Dealing with conflicts

  Different schedulers are essentially separated processes. When all schedulers try to schedule their pods onto the nodes, there might be conflicts.

  One example of the conflicts is resource racing: Suppose there be a pod1 scheduled by my-scheduler requiring 1 CPU's request, and a pod2 scheduled by kube-scheduler (k8s native scheduler, acting as default scheduler) requiring 2 CPU's request, while node-a only has 2.5 free CPU's, if both schedulers all try to put their pods on node-a, then one of them would eventually fail when Kubelet on node-a performs the create action due to insufficient CPU resources.

  This conflict is complex to deal with in api-server and etcd. Our current solution is to let Kubelet to do the conflict check and if the conflict happens, effected pods would be put back to scheduler and waiting to be scheduled again. Implementation details are in later sections.

Where to start: initial design

We definitely want the multi-scheduler design to be a generic mechanism. The following lists the changes we want to make in the first step.

• Add an annotation in pod template: scheduler.alpha.kubernetes.io/name: scheduler-name, this is used to separate pods between schedulers. scheduler-name should match one of the schedulers' scheduler-name

• Add a scheduler-name to each scheduler. It is done by hardcode or as command-line argument. The Kubernetes native scheduler (now kube-scheduler process) would have the name as kube-scheduler

• The scheduler-name plays an important part in separating the pods between different schedulers. Pods are statically dispatched to different schedulers based on scheduler.alpha.kubernetes.io/name: scheduler-name annotation and there should not be any conflicts between different schedulers handling their pods, i.e. one pod must NOT be claimed by more than one scheduler. To be specific, a scheduler can add a pod to its queue if and only if:

  1. The pod has no nodeName, AND

  2. The scheduler-name specified in the pod's annotation scheduler.alpha.kubernetes.io/name: scheduler-name matches the scheduler-name of the scheduler.

     The only one exception is the default scheduler. Any pod that has no scheduler.alpha.kubernetes.io/name: scheduler-name annotation is assumed to be handled by the "default scheduler". In the first version of the multi-scheduler feature, the default scheduler would be the Kubernetes built-in scheduler with scheduler-name as kube-scheduler. The Kubernetes build-in scheduler will claim any pod which has no scheduler.alpha.kubernetes.io/name: scheduler-name annotation or which has scheduler.alpha.kubernetes.io/name: kube-scheduler. In the future, it may be possible to change which scheduler is the default for a given cluster.

• Dealing with conflicts. All schedulers must use predicate functions that are at least as strict as the ones that Kubelet applies when deciding whether to accept a pod, otherwise Kubelet and scheduler may get into an infinite loop where Kubelet keeps rejecting a pod and scheduler keeps re-scheduling it back the same node. To make it easier for people who write new schedulers to obey this rule, we will create a library containing the predicates Kubelet uses. (See issue #12744.)

In summary, in the initial version of this multi-scheduler design, we will achieve the following:

• If a pod has the annotation scheduler.alpha.kubernetes.io/name: kube-scheduler or the user does not explicitly sets this annotation in the template, it will be picked up by default scheduler
• If the annotation is set and refers to a valid scheduler-name, it will be picked up by the scheduler of specified scheduler-name
• If the annotation is set but refers to an invalid scheduler-name, the pod will not be picked by any scheduler. The pod will keep PENDING.

An example

    kind: Pod
    apiVersion: v1
    metadata:
        name: pod-abc   
        labels:
            foo: bar
        annotations:
            scheduler.alpha.kubernetes.io/name: my-scheduler

This pod will be scheduled by "my-scheduler" and ignored by "kube-scheduler". If there is no running scheduler of name "my-scheduler", the pod will never be scheduled.

Next steps

1. Use admission controller to add and verify the annotation, and do some modification if necessary. For example, the admission controller might add the scheduler annotation based on the namespace of the pod, and/or identify if there are conflicting rules, and/or set a default value for the scheduler annotation, and/or reject pods on which the client has set a scheduler annotation that does not correspond to a running scheduler.
2. Dynamic launching scheduler(s) and registering to admission controller (as an external call). This also requires some work on authorization and authentication to control what schedulers can write the /binding subresource of which pods.
3. Optimize the behaviors of priority functions in multi-scheduler scenario. In the case where multiple schedulers have the same predicate and priority functions (for example, when using multiple schedulers for parallelism rather than to customize the scheduling policies), all schedulers would tend to pick the same node as "best" when scheduling identical pods and therefore would be likely to conflict on the Kubelet. To solve this problem, we can pass an optional flag such as --randomize-node-selection=N to scheduler, setting this flag would cause the scheduler to pick randomly among the top N nodes instead of the one with the highest score.

Other issues/discussions related to scheduler design

• #13580: scheduler extension
• #17097: policy config file in pod template
• #16845: scheduling groups of pods
• #17208: guide to writing a new scheduler