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.

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.

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

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

See the user guide in the /docs directory.

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

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. Currently, there are no parameters associated with this strategy. To disable this strategy, the policy should look like:

apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
  "RemoveDuplicates":
     enabled: false

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.

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

This strategy makes sure that pods violating node affinity are removed from nodes. For example, there is podA that was scheduled on nodeA which satisfied the node affinity rule requiredDuringSchedulingIgnoredDuringExecution at the time of scheduling, but over time nodeA no longer satisfies the rule, then if another node nodeB is available that satisfies the node affinity rule, then podA will be evicted from nodeA. The policy file should look like:

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

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

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

• Critical 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.

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.

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 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].

You can reach the contributors of this project at:

• Slack channel
• Mailing list

Learn how to engage with the Kubernetes community on the community page.

This roadmap is not in any particular order.

• Consideration of pod affinity
• Strategy to consider pod life time
• 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

Participation in the Kubernetes community is governed by the Kubernetes Code of Conduct.