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
- User Guide
- Policy and Strategies
- Filter Pods
- Pod Evictions
- Compatibility Matrix
- Getting Involved and Contributing
- Roadmap
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/base/rbac.yaml
kubectl create -f kubernetes/base/configmap.yaml
kubectl create -f kubernetes/job/job.yaml
kubectl create -f kubernetes/base/rbac.yaml
kubectl create -f kubernetes/base/configmap.yaml
kubectl create -f kubernetes/cronjob/cronjob.yaml
Starting with release v0.18.0 there is an official helm chart that can be used to install the descheduler. See the helm chart README for detailed instructions.
The descheduler helm chart is also listed on the artifact hub.
You can use kustomize to install descheduler. See the resources | Kustomize for detailed instructions.
Run As A Job
kustomize build 'github.com/kubernetes-sigs/descheduler/kubernetes/job?ref=master' | kubectl apply -f -
Run As A CronJob
kustomize build 'github.com/kubernetes-sigs/descheduler/kubernetes/cronjob?ref=master' | kubectl apply -f -
See the user guide in the /docs directory.
Descheduler's policy is configurable and includes strategies that can be enabled or disabled.
Eight strategies RemoveDuplicates, LowNodeUtilization, RemovePodsViolatingInterPodAntiAffinity,
RemovePodsViolatingNodeAffinity, RemovePodsViolatingNodeTaints, RemovePodsViolatingTopologySpreadConstraint,
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.
The policy also includes common configuration for all the strategies:
nodeSelector- limiting the nodes which are processedevictLocalStoragePods- allowing to evict pods with local storageignorePvcPods- set whether PVC pods should be evicted or ignored (defaults tofalse)maxNoOfPodsToEvictPerNode- maximum number of pods evicted from each node (summed through all strategies)
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
nodeSelector: prod=dev
evictLocalStoragePods: true
maxNoOfPodsToEvictPerNode: 40
ignorePvcPods: false
strategies:
...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 Kinds. If a pod
has any of these Kinds listed as an OwnerRef, that pod will not be considered for eviction.
Parameters:
| Name | Type |
|---|---|
excludeOwnerKinds |
list(string) |
namespaces |
(see namespace filtering) |
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
Example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"RemoveDuplicates":
enabled: true
params:
removeDuplicates:
excludeOwnerKinds:
- "ReplicaSet"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. If a node's usage is above targetThreshold for any (cpu, memory, or number of pods),
the node is considered over utilized. 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. Note that this
strategy evicts pods from overutilized nodes (those with usage above targetThresholds) to underutilized nodes
(those with usage below thresholds), it will abort if any number of underutilized nodes or overutilized nodes is zero.
Parameters:
| Name | Type |
|---|---|
thresholds |
map(string:int) |
targetThresholds |
map(string:int) |
numberOfNodes |
int |
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
Example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"LowNodeUtilization":
enabled: true
params:
nodeResourceUtilizationThresholds:
thresholds:
"cpu" : 20
"memory": 20
"pods": 20
targetThresholds:
"cpu" : 50
"memory": 50
"pods": 50Policy should pass the following validation checks:
- Only three types of resources are supported:
cpu,memoryandpods. thresholdsortargetThresholdscan not be nil and they must configure exactly the same types of resources.- The valid range of the resource's percentage value is [0, 100]
- Percentage value of
thresholdscan not be greater thantargetThresholdsfor the same resource.
If any of the resource types is not specified, all its thresholds default to 100% to avoid nodes going from underutilized to overutilized.
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.
Parameters:
| Name | Type |
|---|---|
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
namespaces |
(see namespace filtering) |
Example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"RemovePodsViolatingInterPodAntiAffinity":
enabled: trueThis strategy makes sure all pods violating
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.
Parameters:
| Name | Type |
|---|---|
nodeAffinityType |
list(string) |
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
namespaces |
(see namespace filtering) |
Example:
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.
Parameters:
| Name | Type |
|---|---|
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
namespaces |
(see namespace filtering) |
Example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"RemovePodsViolatingNodeTaints":
enabled: trueThis strategy makes sure that pods violating topology spread constraints
are evicted from nodes. Specifically, it tries to evict the minimum number of pods required to balance topology domains to within each constraint's maxSkew.
This strategy requires k8s version 1.18 at a minimum.
By default, this strategy only deals with hard constraints, setting parameter includeSoftConstraints to true will
include soft constraints.
Parameters:
| Name | Type |
|---|---|
includeSoftConstraints |
bool |
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
namespaces |
(see namespace filtering) |
Example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"RemovePodsViolatingTopologySpreadConstraint":
enabled: true
params:
includeSoftConstraints: falseThis 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. Its parameters
include podRestartThreshold, which is the number of restarts at which a pod should be evicted, and includingInitContainers,
which determines whether init container restarts should be factored into that calculation.
Parameters:
| Name | Type |
|---|---|
podRestartThreshold |
int |
includingInitContainers |
bool |
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
namespaces |
(see namespace filtering) |
Example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"RemovePodsHavingTooManyRestarts":
enabled: true
params:
podsHavingTooManyRestarts:
podRestartThreshold: 100
includingInitContainers: trueThis strategy evicts pods that are older than maxPodLifeTimeSeconds.
You can also specify podStatusPhases to only evict pods with specific StatusPhases, currently this parameter is limited
to Running and Pending.
Parameters:
| Name | Type |
|---|---|
maxPodLifeTimeSeconds |
int |
podStatusPhases |
list(string) |
thresholdPriority |
int (see priority filtering) |
thresholdPriorityClassName |
string (see priority filtering) |
namespaces |
(see namespace filtering) |
Example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"PodLifeTime":
enabled: true
params:
podLifeTime:
maxPodLifeTimeSeconds: 86400
podStatusPhases:
- "Pending"The following strategies accept a namespaces parameter which allows to specify a list of including, resp. excluding namespaces:
PodLifeTimeRemovePodsHavingTooManyRestartsRemovePodsViolatingNodeTaintsRemovePodsViolatingNodeAffinityRemovePodsViolatingInterPodAntiAffinityRemoveDuplicatesRemovePodsViolatingTopologySpreadConstraint
For example:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"PodLifeTime":
enabled: true
params:
podLifeTime:
maxPodLifeTimeSeconds: 86400
namespaces:
include:
- "namespace1"
- "namespace2"In the examples PodLifeTime gets executed only over namespace1 and namespace2.
The similar holds for exclude field:
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"PodLifeTime":
enabled: true
params:
podLifeTime:
maxPodLifeTimeSeconds: 86400
namespaces:
exclude:
- "namespace1"
- "namespace2"The strategy gets executed over all namespaces but namespace1 and namespace2.
It's not allowed to compute include with exclude field.
All strategies are able to configure a priority threshold, only pods under the threshold can be evicted. You can
specify this threshold by setting thresholdPriorityClassName(setting the threshold to the value of the given
priority class) or thresholdPriority(directly setting the threshold) parameters. By default, this threshold
is set to the value of system-cluster-critical priority class.
E.g.
Setting thresholdPriority
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"PodLifeTime":
enabled: true
params:
podLifeTime:
maxPodLifeTimeSeconds: 86400
thresholdPriority: 10000Setting thresholdPriorityClassName
apiVersion: "descheduler/v1alpha1"
kind: "DeschedulerPolicy"
strategies:
"PodLifeTime":
enabled: true
params:
podLifeTime:
maxPodLifeTimeSeconds: 86400
thresholdPriorityClassName: "priorityclass1"Note that you can't configure both thresholdPriority and thresholdPriorityClassName, if the given priority class
does not exist, descheduler won't create it and will throw an error.
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 (unless
evictLocalStoragePods: trueis set) - Pods with PVCs are evicted unless
ignorePvcPods: trueis set. - In
LowNodeUtilizationandRemovePodsViolatingInterPodAntiAffinity, pods are evicted by their priority from low to high, and if they have same priority, best effort pods are evicted before burstable and guaranteed pods. - All types of pods with the annotation
descheduler.alpha.kubernetes.io/evictare eligible for eviction. 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.
Setting --v=4 or greater on the Descheduler will log all reasons why any pod is not evictable.
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.
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 |
|---|---|
| v0.20 | v1.20 |
| v0.19 | v1.19 |
| v0.18 | v1.18 |
| v0.10 | v1.17 |
| v0.4-v0.9 | v1.9+ |
| v0.1-v0.3 | v1.7-v1.8 |
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:
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 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.