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# Descheduler for Kubernetes

## Introduction

Scheduling in Kubernetes is the process of binding pending pods to nodes, and is performed by
a component of Kubernetes called kube-scheduler. The scheduler's decisions, whether or where a
pod can or can not be scheduled, are guided by its configurable policy which comprises of set of
rules, called predicates and priorities. The scheduler's decisions are influenced by its view of
a Kubernetes cluster at that point of time when a new pod appears for scheduling.
As Kubernetes clusters are very dynamic and their state changes over time, there may be desire
to move already running pods to some other nodes for various reasons:

* Some nodes are under or over utilized.
* The original scheduling decision does not hold true any more, as taints or labels are added to
or removed from nodes, pod/node affinity requirements are not satisfied any more.
* Some nodes failed and their pods moved to other nodes.
* New nodes are added to clusters.

Consequently, there might be several pods scheduled on less desired nodes in a cluster.
Descheduler, based on its policy, finds pods that can be moved and evicts them. Please
note, in current implementation, descheduler does not schedule replacement of evicted pods
but relies on the default scheduler for that.

## Quick Start

The descheduler can be run as a Job or CronJob inside of a k8s cluster. It has the
advantage of being able to be run multiple times without needing user intervention.
The descheduler pod is run as a critical pod in the `kube-system` namespace to avoid
being evicted by itself or by the kubelet.

### Run As A Job

```
kubectl create -f kubernetes/rbac.yaml
kubectl create -f kubernetes/configmap.yaml
kubectl create -f kubernetes/job.yaml
```

### Run As A CronJob

```
kubectl create -f kubernetes/rbac.yaml
kubectl create -f kubernetes/configmap.yaml
kubectl create -f kubernetes/cronjob.yaml
```

## User Guide

See the [user guide](docs/user-guide.md) in the `/docs` directory.

## Policy and Strategies

Descheduler's policy is configurable and includes strategies that can be enabled or disabled.
Seven strategies `RemoveDuplicates`, `LowNodeUtilization`, `RemovePodsViolatingInterPodAntiAffinity`,
`RemovePodsViolatingNodeAffinity`, `RemovePodsViolatingNodeTaints`, `RemovePodsHavingTooManyRestarts`, and `PodLifeTime`
are currently implemented. As part of the policy, the parameters associated with the strategies can be configured too.
By default, all strategies are enabled.

### RemoveDuplicates

This strategy makes sure that there is only one pod associated with a Replica Set (RS),
Replication Controller (RC), Deployment, or Job running on the same node. If there are more,
those duplicate pods are evicted for better spreading of pods in a cluster. This issue could happen
if some nodes went down due to whatever reasons, and pods on them were moved to other nodes leading to
more than one pod associated with a RS or RC, for example, running on the same node. Once the failed nodes
are ready again, this strategy could be enabled to evict those duplicate pods.

It provides one optional parameter, `ExcludeOwnerKinds`, which is a list of OwnerRef `Kind`s. If a pod
has any of these `Kind`s listed as an `OwnerRef`, that pod will not be considered for eviction.

```
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemoveDuplicates":
     enabled: true
     params:
       removeDuplicates:
         excludeOwnerKinds:
         - "ReplicaSet"
```

### LowNodeUtilization

This strategy finds nodes that are under utilized and evicts pods, if possible, from other nodes
in the hope that recreation of evicted pods will be scheduled on these underutilized nodes. The
parameters of this strategy are configured under `nodeResourceUtilizationThresholds`.

The under utilization of nodes is determined by a configurable threshold `thresholds`. The threshold
`thresholds` can be configured for cpu, memory, and number of pods in terms of percentage. If a node's
usage is below threshold for all (cpu, memory, and number of pods), the node is considered underutilized.
Currently, pods request resource requirements are considered for computing node resource utilization.

There is another configurable threshold, `targetThresholds`, that is used to compute those potential nodes
from where pods could be evicted. Any node, between the thresholds, `thresholds` and `targetThresholds` is
considered appropriately utilized and is not considered for eviction. The threshold, `targetThresholds`,
can be configured for cpu, memory, and number of pods too in terms of percentage.

These thresholds, `thresholds` and `targetThresholds`, could be tuned as per your cluster requirements.
Here is an example of a policy for this strategy:

```
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "LowNodeUtilization":
     enabled: true
     params:
       nodeResourceUtilizationThresholds:
         thresholds:
           "cpu" : 20
           "memory": 20
           "pods": 20
         targetThresholds:
           "cpu" : 50
           "memory": 50
           "pods": 50
```

There is another parameter associated with the `LowNodeUtilization` strategy, called `numberOfNodes`.
This parameter can be configured to activate the strategy only when the number of under utilized nodes
are above the configured value. This could be helpful in large clusters where a few nodes could go
under utilized frequently or for a short period of time. By default, `numberOfNodes` is set to zero.

### RemovePodsViolatingInterPodAntiAffinity

This strategy makes sure that pods violating interpod anti-affinity are removed from nodes. For example,
if there is podA on a node and podB and podC (running on the same node) have anti-affinity rules which prohibit
them to run on the same node, then podA will be evicted from the node so that podB and podC could run. This
issue could happen, when the anti-affinity rules for podB and podC are created when they are already running on
node. Currently, there are no parameters associated with this strategy. To disable this strategy, the
policy should look like:

```
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemovePodsViolatingInterPodAntiAffinity":
     enabled: false
```

### RemovePodsViolatingNodeAffinity

This strategy makes sure all pods violating
[node affinity](https://kubernetes.io/docs/concepts/configuration/assign-pod-node/#node-affinity)
are eventually removed from nodes. Node affinity rules allow a pod to specify
`requiredDuringSchedulingIgnoredDuringExecution` type, which tells the scheduler
to respect node affinity when scheduling the pod but kubelet to ignore
in case node changes over time and no longer respects the affinity.
When enabled, the strategy serves as a temporary implementation
of `requiredDuringSchedulingRequiredDuringExecution` and evicts pod for kubelet
that no longer respects node affinity.

For example, there is podA scheduled on nodeA which satisfies the node
affinity rule `requiredDuringSchedulingIgnoredDuringExecution` at the time
of scheduling. Over time nodeA stops to satisfy the rule. When the strategy gets
executed and there is another node available that satisfies the node affinity rule,
podA gets evicted from nodeA.

The policy file should look like:

```
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemovePodsViolatingNodeAffinity":
    enabled: true
    params:
      nodeAffinityType:
      - "requiredDuringSchedulingIgnoredDuringExecution"
```

### RemovePodsViolatingNodeTaints

This strategy makes sure that pods violating NoSchedule taints on nodes are removed. For example there is a
pod "podA" with a toleration to tolerate a taint ``key=value:NoSchedule`` scheduled and running on the tainted
node. If the node's taint is subsequently updated/removed, taint is no longer satisfied by its pods' tolerations
and will be evicted. The policy file should look like:

````
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemovePodsViolatingNodeTaints":
    enabled: true
````

### RemovePodsHavingTooManyRestarts

This strategy makes sure that pods having too many restarts are removed from nodes. For example a pod with EBS/PD that can't get the volume/disk attached to the instance, then the pod should be re-scheduled to other nodes.

```
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemovePodsHavingTooManyRestarts":
     enabled: true
     params:
       podsHavingTooManyRestarts:
         podRestartThreshold: 100
         includingInitContainers: true
```

### PodLifeTime

This strategy evicts pods that are older than `.strategies.PodLifeTime.params.maxPodLifeTimeSeconds` The policy
file should look like:

````
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "PodLifeTime":
     enabled: true
     params:
        maxPodLifeTimeSeconds: 86400
````

## Pod Evictions

When the descheduler decides to evict pods from a node, it employs the following general mechanism:

* [Critical pods](https://kubernetes.io/docs/tasks/administer-cluster/guaranteed-scheduling-critical-addon-pods/) (with priorityClassName set to system-cluster-critical or system-node-critical) are never evicted.
* Pods (static or mirrored pods or stand alone pods) not part of an RC, RS, Deployment or Job are
never evicted because these pods won't be recreated.
* Pods associated with DaemonSets are never evicted.
* Pods with local storage are never evicted.
* Best efforts pods are evicted before burstable and guaranteed pods.
* All types of pods with the annotation descheduler.alpha.kubernetes.io/evict are evicted. This
  annotation is used to override checks which prevent eviction and users can select which pod is evicted.
  Users should know how and if the pod will be recreated.

### Pod Disruption Budget (PDB)

Pods subject to a Pod Disruption Budget(PDB) are not evicted if descheduling violates its PDB. The pods
are evicted by using the eviction subresource to handle PDB.

## Compatibility Matrix
The below compatibility matrix shows the k8s client package(client-go, apimachinery, etc) versions that descheduler
is compiled with. At this time descheduler does not have a hard dependency to a specific k8s release. However a
particular descheduler release is only tested against the three latest k8s minor versions. For example descheduler
v0.18 should work with k8s v1.18, v1.17, and v1.16.

Starting with descheduler release v0.18 the minor version of descheduler matches the minor version of the k8s client
packages that it is compiled with.

Descheduler  | Supported Kubernetes Version
-------------|-----------------------------
v0.18        | v1.18
v0.10        | v1.17
v0.4-v0.9    | v1.9+
v0.1-v0.3    | v1.7-v1.8


## Getting Involved and Contributing

Are you interested in contributing to descheduler? We, the
maintainers and community, would love your suggestions, contributions, and help!
Also, the maintainers can be contacted at any time to learn more about how to get
involved.

To get started writing code see the [contributor guide](docs/contributor-guide.md) in the `/docs` directory.

In the interest of getting more new people involved we tag issues with
[`good first issue`][good_first_issue].
These are typically issues that have smaller scope but are good ways to start
to get acquainted with the codebase.

We also encourage ALL active community participants to act as if they are
maintainers, even if you don't have "official" write permissions. This is a
community effort, we are here to serve the Kubernetes community. If you have an
active interest and you want to get involved, you have real power! Don't assume
that the only people who can get things done around here are the "maintainers".

We also would love to add more "official" maintainers, so show us what you can
do!

This repository uses the Kubernetes bots. See a full list of the commands [here][prow].

### Communicating With Contributors

You can reach the contributors of this project at:

- [Slack channel](https://kubernetes.slack.com/messages/sig-scheduling)
- [Mailing list](https://groups.google.com/forum/#!forum/kubernetes-sig-scheduling)

Learn how to engage with the Kubernetes community on the [community page](http://kubernetes.io/community/).

## Roadmap

This roadmap is not in any particular order.

* Consideration of pod affinity
* Strategy to consider number of pending pods
* Integration with cluster autoscaler
* Integration with metrics providers for obtaining real load metrics
* Consideration of Kubernetes's scheduler's predicates


### Code of conduct

Participation in the Kubernetes community is governed by the [Kubernetes Code of Conduct](code-of-conduct.md).
