Scheduling

Learn about scheduling workloads with Karpenter

If your pods have no requirements for how or where to run, you can let Karpenter choose nodes from the full range of available cloud provider resources. However, by taking advantage of Karpenter’s model of layered constraints, you can be sure that the precise type and amount of resources needed are available to your pods. Reasons for constraining where your pods run could include:

  • Needing to run in zones where dependent applications or storage are available
  • Requiring certain kinds of processors or other hardware
  • Wanting to use techniques like topology spread to help ensure high availability

Your Cloud Provider defines the first layer of constraints, including all instance types, architectures, zones, and purchase types available to its cloud. The cluster administrator adds the next layer of constraints by creating one or more NodePools. The final layer comes from you adding specifications to your Kubernetes pod deployments. Pod scheduling constraints must fall within a NodePool’s constraints or the pods will not deploy. For example, if the NodePool sets limits that allow only a particular zone to be used, and a pod asks for a different zone, it will not be scheduled.

Constraints you can request include:

  • Resource requests: Request that certain amount of memory or CPU be available.
  • Node selection: Choose to run on a node that is has a particular label (nodeSelector).
  • Node affinity: Draws a pod to run on nodes with particular attributes (affinity).
  • Topology spread: Use topology spread to help ensure availability of the application.
  • Pod affinity/anti-affinity: Draws pods towards or away from topology domains based on the scheduling of other pods.

Karpenter supports standard Kubernetes scheduling constraints. This allows you to define a single set of rules that apply to both existing and provisioned capacity.

Resource requests

Within a Pod spec, you can both make requests and set limits on resources a pod needs, such as CPU and memory. For example:

apiVersion: v1
kind: Pod
metadata:
  name: myapp
spec:
  containers:
  - name: app
    image: myimage
    resources:
      requests:
        memory: "128Mi"
        cpu: "500m"
      limits:
        memory: "256Mi"
        cpu: "1000m"

In this example, the container is requesting 128MiB of memory and .5 CPU. Its limits are set to 256MiB of memory and 1 CPU. Instance type selection math only uses requests, but limits may be configured to enable resource oversubscription.

See Managing Resources for Containers for details on resource types supported by Kubernetes, Specify a memory request and a memory limit for examples of memory requests, and NodePools for a list of supported resources.

Accelerators/GPU Resources

Accelerator (e.g., GPU) values include

  • nvidia.com/gpu
  • amd.com/gpu
  • aws.amazon.com/neuron
  • habana.ai/gaudi

Karpenter supports accelerators, such as GPUs.

Additionally, include a resource requirement in the workload manifest. This will cause the GPU dependent pod to be scheduled onto the appropriate node.

Here is an example of an accelerator resource in a workload manifest (e.g., pod):

spec:
  template:
    spec:
      containers:
      - resources:
          limits:
            nvidia.com/gpu: "1"

Pod ENI Resources (Security Groups for Pods)

Pod ENI is a feature of the AWS VPC CNI Plugin which allows an Elastic Network Interface (ENI) to be allocated directly to a Pod. When enabled, the vpc.amazonaws.com/pod-eni extended resource is added to supported nodes. The Pod ENI feature can be used independently, but is most often used in conjunction with Security Groups for Pods. Follow the below instructions to enable support for Pod ENI and/or Security Groups for Pods in Karpenter.

Here is an example of a pod-eni resource defined in a deployment manifest:

spec:
  template:
    spec:
      containers:
      - resources:
          limits:
            vpc.amazonaws.com/pod-eni: "1"

Selecting nodes

With nodeSelector you can ask for a node that matches selected key-value pairs. This can include well-known labels or custom labels you create yourself.

You can use affinity to define more complicated constraints, see Node Affinity for the complete specification.

Labels

Well-known labels may be specified as NodePool requirements or pod scheduling constraints. You can also define your own custom labels by specifying requirements or labels on your NodePool and select them using nodeAffinity or nodeSelectors on your Pods.

Well-Known Labels

LabelExampleDescription
topology.kubernetes.io/zoneus-east-2aZones are defined by your cloud provider (aws)
node.kubernetes.io/instance-typeg4dn.8xlargeInstance types are defined by your cloud provider (aws)
node.kubernetes.io/windows-build10.0.17763Windows OS build in the format “MajorVersion.MinorVersion.BuildNumber”. Can be 10.0.17763 for WS2019, or 10.0.20348 for WS2022. (k8s)
kubernetes.io/oslinuxOperating systems are defined by GOOS values on the instance
kubernetes.io/archamd64Architectures are defined by GOARCH values on the instance
karpenter.sh/capacity-typespotCapacity types include spot, on-demand
karpenter.k8s.aws/instance-hypervisornitro[AWS Specific] Instance types that use a specific hypervisor
karpenter.k8s.aws/instance-encryption-in-transit-supportedtrue[AWS Specific] Instance types that support (or not) in-transit encryption
karpenter.k8s.aws/instance-categoryg[AWS Specific] Instance types of the same category, usually the string before the generation number
karpenter.k8s.aws/instance-generation4[AWS Specific] Instance type generation number within an instance category
karpenter.k8s.aws/instance-familyg4dn[AWS Specific] Instance types of similar properties but different resource quantities
karpenter.k8s.aws/instance-size8xlarge[AWS Specific] Instance types of similar resource quantities but different properties
karpenter.k8s.aws/instance-cpu32[AWS Specific] Number of CPUs on the instance
karpenter.k8s.aws/instance-cpu-manufactureraws[AWS Specific] Name of the CPU manufacturer
karpenter.k8s.aws/instance-memory131072[AWS Specific] Number of mebibytes of memory on the instance
karpenter.k8s.aws/instance-ebs-bandwidth9500[AWS Specific] Number of maximum megabits of EBS available on the instance
karpenter.k8s.aws/instance-network-bandwidth131072[AWS Specific] Number of baseline megabits available on the instance
karpenter.k8s.aws/instance-pods110[AWS Specific] Number of pods the instance supports
karpenter.k8s.aws/instance-gpu-namet4[AWS Specific] Name of the GPU on the instance, if available
karpenter.k8s.aws/instance-gpu-manufacturernvidia[AWS Specific] Name of the GPU manufacturer
karpenter.k8s.aws/instance-gpu-count1[AWS Specific] Number of GPUs on the instance
karpenter.k8s.aws/instance-gpu-memory16384[AWS Specific] Number of mebibytes of memory on the GPU
karpenter.k8s.aws/instance-local-nvme900[AWS Specific] Number of gibibytes of local nvme storage on the instance

User-Defined Labels

Karpenter is aware of several well-known labels, deriving them from instance type details. If you specify a nodeSelector or a required nodeAffinity using a label that is not well-known to Karpenter, it will not launch nodes with these labels and pods will remain pending. For Karpenter to become aware that it can schedule for these labels, you must specify the label in the NodePool requirements with the Exists operator:

requirements:
  - key: user.defined.label/type
    operator: Exists

Node selectors

Here is an example of a nodeSelector for selecting nodes:

nodeSelector:
  topology.kubernetes.io/zone: us-west-2a
  karpenter.sh/capacity-type: spot

This example features a well-known label (topology.kubernetes.io/zone) and a label that is well known to Karpenter (karpenter.sh/capacity-type).

If you want to create a custom label, you should do that at the NodePool level. Then the pod can declare that custom label.

See nodeSelector in the Kubernetes documentation for details.

Node affinity

Examples below illustrate how to use Node affinity to include (In) and exclude (NotIn) objects. See Node affinity for details. When setting rules, the following Node affinity types define how hard or soft each rule is:

  • requiredDuringSchedulingIgnoredDuringExecution: This is a hard rule that must be met.
  • preferredDuringSchedulingIgnoredDuringExecution: This is a preference, but the pod can run on a node where it is not guaranteed.

The IgnoredDuringExecution part of each tells the pod to keep running, even if conditions change on the node so the rules no longer matched. You can think of these concepts as required and preferred, since Kubernetes never implemented other variants of these rules.

All examples below assume that the NodePool doesn’t have constraints to prevent those zones from being used. The first constraint says you could use us-west-2a or us-west-2b, the second constraint makes it so only us-west-2b can be used.

 affinity:
   nodeAffinity:
     requiredDuringSchedulingIgnoredDuringExecution:
       nodeSelectorTerms:
         - matchExpressions:
           - key: "topology.kubernetes.io/zone"
             operator: "In"
             values: ["us-west-2a", "us-west-2b"]
           - key: "topology.kubernetes.io/zone"
             operator: "In"
             values: ["us-west-2b"]

Changing the second operator to NotIn would allow the pod to run in us-west-2a only:

           - key: "topology.kubernetes.io/zone"
             operator: "In"
             values: ["us-west-2a", "us-west-2b"]
           - key: "topology.kubernetes.io/zone"
             operator: "NotIn"
             values: ["us-west-2b"]

Continuing to add to the example, nodeAffinity lets you define terms so if one term doesn’t work it goes to the next one. Here, if us-west-2a is not available, the second term will cause the pod to run on a spot instance in us-west-2d.

 affinity:
   nodeAffinity:
     requiredDuringSchedulingIgnoredDuringExecution:
       nodeSelectorTerms:
         - matchExpressions: # OR
           - key: "topology.kubernetes.io/zone" # AND
             operator: "In"
             values: ["us-west-2a", "us-west-2b"]
           - key: "topology.kubernetes.io/zone" # AND
             operator: "NotIn"
             values: ["us-west-2b"]
         - matchExpressions: # OR
           - key: "karpenter.sh/capacity-type" # AND
             operator: "In"
             values: ["spot"]
           - key: "topology.kubernetes.io/zone" # AND
             operator: "In"
             values: ["us-west-2d"]

In general, Karpenter will go through each of the nodeSelectorTerms in order and take the first one that works. However, if Karpenter fails to provision on the first nodeSelectorTerms, it will try again using the second one. If they all fail, Karpenter will fail to provision the pod. Karpenter will backoff and retry over time. So if capacity becomes available, it will schedule the pod without user intervention.

Taints and tolerations

Taints are the opposite of affinity. Setting a taint on a node tells the scheduler to not run a pod on it unless the pod has explicitly said it can tolerate that taint. This example shows a NodePool that was set up with a taint for only running pods that require a GPU, such as the following:

apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: gpu
spec:
  template:
    spec:
      requirements:
      - key: karpenter.k8s.aws/instance-family
        operator: In
        values:
          - p3
      taints:
      - key: nvidia.com/gpu
        value: true
        effect: "NoSchedule"

For a pod to request to run on a node that has this NodePool, it could set a toleration as follows:

apiVersion: v1
kind: Pod
metadata:
  name: mygpupod
spec:
  containers:
  - name: gpuapp
    resources:
      requests:
        nvidia.com/gpu: 1
      limits:
        nvidia.com/gpu: 1
    image: mygpucontainer
  tolerations:
  - key: "nvidia.com/gpu"
    operator: "Exists"
    effect: "NoSchedule"

See Taints and Tolerations in the Kubernetes documentation for details.

Topology Spread

By using the Kubernetes topologySpreadConstraints you can ask the NodePool to have pods push away from each other to limit the blast radius of an outage. Think of it as the Kubernetes evolution for pod affinity: it lets you relate pods with respect to nodes while still allowing spread. For example:

spec:
  topologySpreadConstraints:
    - maxSkew: 1
      topologyKey: "topology.kubernetes.io/zone"
      whenUnsatisfiable: ScheduleAnyway
      labelSelector:
        matchLabels:
          dev: jjones
    - maxSkew: 1
      topologyKey: "kubernetes.io/hostname"
      whenUnsatisfiable: ScheduleAnyway
      labelSelector:
        matchLabels:
          dev: jjones
    - maxSkew: 1
      topologyKey: "karpenter.sh/capacity-type"
      whenUnsatisfiable: ScheduleAnyway
      labelSelector:
        matchLabels:
          dev: jjones

Adding this to your podspec would result in:

  • Pods being spread across zones, hosts, and capacity-type (topologyKey).
  • The dev labelSelector will include all pods with the label of dev=jjones in topology calculations. It is recommended to use a selector to match all pods in a deployment.
  • No more than one pod difference in the number of pods on each host (maxSkew). For example, if there were three nodes and five pods the pods could be spread 1, 2, 2 or 2, 1, 2 and so on. If instead the spread were 5, pods could be 5, 0, 0 or 3, 2, 0, or 2, 1, 2 and so on.

The three supported topologyKey values that Karpenter supports are:

  • topology.kubernetes.io/zone
  • kubernetes.io/hostname
  • karpenter.sh/capacity-type

See Pod Topology Spread Constraints for details.

Pod affinity/anti-affinity

By using the podAffinity and podAntiAffinity configuration on a pod spec, you can inform the Karpenter scheduler of your desire for pods to schedule together or apart with respect to different topology domains. For example:

spec:
  affinity:
    podAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
      - labelSelector:
          matchExpressions:
          - key: system
            operator: In
            values:
            - backend
        topologyKey: topology.kubernetes.io/zone
    podAntiAffinity:
      requiredDuringSchedulingIgnoredDuringExecution:
      - labelSelector:
          matchLabels:
            app: inflate
        topologyKey: kubernetes.io/hostname

The above pod affinity rule would cause the pod to only schedule in zones where a pod with the label system=backend is already running.

The anti-affinity rule would cause it to avoid running on any node with a pod labeled app=inflate. If this anti-affinity term was on a deployment pod spec along with a matching app=inflate label, it would prevent more than one pod from the deployment from running on any single node.

See Inter-pod affinity and anti-affinity in the Kubernetes documentation for details.

Persistent Volume Topology

Karpenter automatically detects storage scheduling requirements and includes them in node launch decisions.

In the following example, the StorageClass defines zonal topologies for us-west-2a and us-west-2b and binding mode WaitForFirstConsumer. When the pod is created, Karpenter follows references from the Pod to PersistentVolumeClaim to StorageClass and identifies that this pod requires storage in us-west-2a and us-west-2b. It randomly selects us-west-2a, provisions a node in that zone, and waits for kube-scheduler to bind the pod to the node. The CSI driver creates a PersistentVolume according to the PersistentVolumeClaim and gives it a node affinity rule for us-west-2a.

Later on, the pod is deleted and a new pod is created that requests the same claim. This time, Karpenter identifies that a PersistentVolume already exists for the PersistentVolumeClaim, and includes its zone us-west-2a in the pod’s scheduling requirements.

apiVersion: v1
kind: Pod
metadata:
  name: app
spec:
  containers: ...
  volumes:
    - name: storage
      persistentVolumeClaim:
        claimName: ebs-claim
---
kind: StorageClass
apiVersion: storage.k8s.io/v1
metadata:
  name: ebs
provisioner: ebs.csi.aws.com
volumeBindingMode: WaitForFirstConsumer
allowedTopologies:
- matchLabelExpressions:
  - key: topology.ebs.csi.aws.com/zone
    values: ["us-west-2a", "us-west-2b"]
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: ebs-claim
spec:
  accessModes:
    - ReadWriteOnce
  storageClassName: ebs
  resources:
    requests:
      storage: 4Gi

Weighted NodePools

Karpenter allows you to order your NodePools using the .spec.weight field so that the Karpenter scheduler will attempt to schedule one NodePool before another.

Savings Plans and Reserved Instances

If you have purchased a Savings Plan or Reserved Instances, you may want to tell Karpenter to prioritize this reserved capacity ahead of other instance types.

To enable this, you will need to tell the Karpenter controllers which instance types to prioritize and what is the maximum amount of capacity that should be provisioned using those instance types. We can set the .spec.limits field on the NodePool to limit the capacity that can be launched by this NodePool. Combined with the .spec.weight value, we can tell Karpenter to pull from instance types in the reserved NodePool before defaulting to generic instance types.

apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: reserved-instance
spec:
  weight: 50
  limits:
    cpu: 100
  template:
    spec:
      requirements:
      - key: "node.kubernetes.io/instance-type"
        operator: In
        values: ["c4.large"]
---
apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: default
spec:
  template:
    spec:
      requirements:
      - key: karpenter.sh/capacity-type
        operator: In
        values: ["spot", "on-demand"]
      - key: kubernetes.io/arch
        operator: In
        values: ["amd64"]

Fallback

Pods that do not specify node selectors or affinities can potentially be assigned to any node with any configuration. There may be cases where you require these pods to schedule to a specific capacity type or architecture but assigning the relevant node selectors or affinities to all these workload pods may be too tedious or infeasible. Instead, we want to define a cluster-wide default configuration for nodes launched using Karpenter.

By assigning a higher .spec.weight value and restricting a NodePool to a specific capacity type or architecture, we can set default configuration for the nodes launched by pods that don’t have node configuration restrictions.

apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: default
spec:
  weight: 50
  template:
    spec:
      requirements:
      - key: karpenter.sh/capacity-type
        operator: In
        values: ["spot", "on-demand"]
      - key: kubernetes.io/arch
        operator: In
        values: ["amd64"]
---
apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: arm64-specific
spec:
  template:
    spec:
      requirements:
      - key: karpenter.sh/capacity-type
        operator: In
        values: ["spot", "on-demand"]
      - key: kubernetes.io/arch
        operator: In
        values: ["arm64"]
      - key: node.kubernetes.io/instance-type
        operator: In
        values: ["a1.large", "a1.xlarge"]

Advanced Scheduling Techniques

Scheduling based on Node Resources

You may want pods to be able to request resources of nodes that Kubernetes natively does not provide as a schedulable resource or that are aspects of certain nodes like High Performance Networking or NVME Local Storage. You can use Karpenter’s Well-Known Labels to accomplish this.

These can further be applied at the NodePool or Workload level using Requirements, NodeSelectors or Affinities

Pod example of requiring any NVME disk:

...
 affinity:
   nodeAffinity:
     requiredDuringSchedulingIgnoredDuringExecution:
       nodeSelectorTerms:
         - matchExpressions:
           - key: "karpenter.k8s.aws/instance-local-nvme"
             operator: "Exists"
...

NodePool Example:

...
requirement:
  - key: "karpenter.k8s.aws/instance-local-nvme"
    operator: "Exists"
...

Pod example of requiring at least 100GB of NVME disk:

...
 affinity:
   nodeAffinity:
     requiredDuringSchedulingIgnoredDuringExecution:
       nodeSelectorTerms:
         - matchExpressions:
            - key: "karpenter.k8s.aws/instance-local-nvme"
              operator: Gt
              values: ["99"]
...

NodePool Example:

...
requirement:
  - key: "karpenter.k8s.aws/instance-local-nvme"
    operator: Gt
    values: ["99"]
...

Pod example of requiring at least 50 Gbps of network bandwidth:

...
 affinity:
   nodeAffinity:
     requiredDuringSchedulingIgnoredDuringExecution:
       nodeSelectorTerms:
         - matchExpressions:
            - key: "karpenter.k8s.aws/instance-network-bandwidth"
              operator: Gt
              values: ["49999"]
...

NodePool Example:

...
requirement:
  - key: "karpenter.k8s.aws/instance-network-bandwidth"
    operator: Gt
    values: ["49999"]
...

Exists Operator

The Exists operator can be used on a NodePool to provide workload segregation across nodes.

apiVersion: karpenter.sh/v1beta1
kind: NodePool
spec:
  template:
    spec:
      requirements:
        - key: company.com/team
          operator: Exists
...

With this requirement on the NodePool, workloads can specify the same key (e.g. company.com/team) with custom values (e.g. team-a, team-b, etc.) as a required nodeAffinity or nodeSelector. Karpenter will then apply the key/value pair to nodes it launches dynamically based on the pod’s node requirements.

If each set of pods that can schedule with this NodePool specifies this key in its nodeAffinity or nodeSelector, you can isolate pods onto different nodes based on their values. This provides a way to more dynamically isolate workloads without requiring a unique NodePool for each workload subset.

For example, providing the following nodeSelectors would isolate the pods for each of these deployments on different nodes.

Team A Deployment

apiVersion: v1
kind: Deployment
metadata:
  name: team-a-deployment
spec:
  replicas: 5
  template:
    spec:
      nodeSelector:
        company.com/team: team-a

Team A Node

apiVersion: v1
kind: Node
metadata:
  labels:
    company.com/team: team-a

Team B Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: team-b-deployment
spec:
  replicas: 5
  template:
    spec:
      nodeSelector:
        company.com/team: team-b

Team B Node

apiVersion: v1
kind: Node
metadata:
  labels:
    company.com/team: team-b

On-Demand/Spot Ratio Split

Taking advantage of Karpenter’s ability to assign labels to node and using a topology spread across those labels enables a crude method for splitting a workload across on-demand and spot instances in a desired ratio.

To do this, we create one NodePool each for spot and on-demand with disjoint values for a unique new label called capacity-spread. In the example below, we provide four unique values for the spot NodePool and one value for the on-demand NodePool. When we spread across our new label evenly, we’ll end up with a ratio of 4:1 spot to on-demand nodes.

NodePools

apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: spot
spec:
  template:
    spec:
      requirements:
      - key: "karpenter.sh/capacity-type"
        operator: In
        values: ["spot"]
      - key: capacity-spread
        operator: In
        values:
        - "2"
        - "3"
        - "4"
        - "5"
---
apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: on-demand
spec:
  template:
    spec:
      requirements:
      - key: "karpenter.sh/capacity-type"
        operator: In
        values: ["on-demand"]
      - key: capacity-spread
        operator: In
        values:
        - "1"

Workload Topology Spread Constraint

topologySpreadConstraints:
- maxSkew: 1
  topologyKey: capacity-spread
  whenUnsatisfiable: DoNotSchedule
  labelSelector:
    ...
Last modified May 29, 2024: chore: Release v0.37.0 (#6278) (b95b3cf4)