Autoscaling in a Managed Kubernetes cluster

For your information

Autoscaling is unavailable:

for node groups with GPU without drivers;
node groups on dedicated servers.

In a Managed Kubernetes cluster, you can use Cluster Autoscaler or Karpenter for node group autoscaling. They help use cluster resources efficiently: depending on the cluster load, the number of nodes in a group will automatically decrease or increase. Cluster Autoscaler is installed automatically when creating a cluster; to get started, simply enable it. Karpenter must be pre-installed and configured.

When using autoscaling tools, consider the recommendations.

For pod autoscaling in Managed Kubernetes, Metrics Server is used.

Recommendations

For optimal autoscaling performance, we recommend:

do not use more than one autoscaling tool at the same time;
make sure that the project has quotas for vCPU, RAM, GPU, and disk size to create the maximum number of nodes in the group;
specify resource requests in pod manifests. For more information, see the Resource Management for Pods and Containers guide in Kubernetes documentation;
configure PodDisruptionBudget for pods that cannot tolerate disruptions. This will help avoid downtime during migration between nodes;
do not change node resources manually via the control panel. Cluster Autoscaler and Karpenter will not take these changes into account;
when using Cluster Autoscaler, make sure that the nodes in the group have the same configuration and labels.

Autoscaling using Cluster Autoscaler

Cluster Autoscaler does not need to be installed in the cluster — it is installed automatically when creating a cluster. To use Cluster Autoscaler in a cluster, enable node group autoscaling. After autoscaling is enabled, default settings are used, but you can configure Cluster Autoscaler for each node group.

Operating principle

Cluster Autoscaler works with existing node groups and pre-selected configurations. If a node group is in the ACTIVE status, Cluster Autoscaler checks every 10 seconds whether there are pods in the PENDING status and analyzes the load — requests from pods for vCPU, RAM, and GPU. Depending on the check results, nodes are added or deleted. The node group enters the PENDING_SCALE_UP or PENDING_SCALE_DOWN status during this time. The cluster status during autoscaling is ACTIVE. For more information about cluster statuses, see the View cluster status guide.

The minimum and maximum number of nodes in a group can be set when enabling autoscaling — Cluster Autoscaler will change the number of nodes only within these limits. If at least two worker nodes remain in other node groups of the cluster, you can configure autoscaling to zero nodes.

Adding a node

If there are pods in the PENDING status and the cluster does not have enough free resources to place them, the required number of nodes is added to the cluster. In a cluster with Kubernetes version 1.28 or higher, Cluster Autoscaler will work across multiple groups simultaneously and distribute nodes evenly.

note

For example, you have two node groups with autoscaling enabled. The cluster load has increased and requires four nodes to be added. Two new nodes will be created in each node group simultaneously.

In a cluster with Kubernetes version 1.27 or lower, nodes are added one at a time per check cycle.

Deleting a node

If there are no pods in the PENDING status, Cluster Autoscaler checks the amount of resources requested by pods.

If the requested amount of resources for pods on a node is less than 50% of its resources, Cluster Autoscaler marks the node as unneeded. If the number of resource requests on the node does not increase within 10 minutes, Cluster Autoscaler will check if the pods can be migrated to other nodes.

Cluster Autoscaler will not move pods and, consequently, will not delete a node if one of the following conditions is met:

pods use PodDisruptionBudget;
In kube-system pods there is no PodDisruptionBudget;
pods are created without a controller — for example, Deployment, ReplicaSet, StatefulSet;
pods use local storage;
there are no resources on other nodes for the pod requests;
there is a mismatch of nodeSelector, affinity and anti-affinity rules, or other parameters.

You can allow such pods to be moved — to do this, add the following annotation:

cluster-autoscaler.kubernetes.io/safe-to-evict: "true"

If there are no restrictions, the pods will be migrated, and the underloaded nodes will be removed. Nodes are removed one by one per check cycle.

Autoscaling to zero nodes

In a node group, you can configure autoscaling to zero nodes—if the load is low, all nodes in the group are removed. The node group card with all settings is not removed. When the load increases, nodes can be added to this node group again.

Autoscaling to zero nodes works only if there are at least two working nodes remaining in other node groups in the cluster. Working nodes must remain in the cluster to host system components required for cluster operation.

note

For example, autoscaling to zero nodes will not work if the cluster has:

two node groups with one working node in each;
one node group with two working nodes.

When there are no nodes in a group, you do not pay for unused resources.

Enable autoscaling using Cluster Autoscaler

For your information

If you set the minimum number of nodes in a group greater than the current number, it will not increase to the lower limit immediately. The node group will only scale after pods in the PENDING status appear. The same applies to the upper limit of nodes in a group: if the current number of nodes exceeds the upper limit, deletion will start only after checking the pods.

You can enable autoscaling using Cluster Autoscaler in the control panel, via the Managed Kubernetes API, or via Terraform.

In the control panel, in the top menu, click Products and select Managed Kubernetes.
In the Clusters section, open the cluster page → Cluster topology tab.
From the menu of the node group, select Change number of nodes.
In the Number of nodes field, open the With autoscaling tab.
Set the minimum and maximum number of nodes in the group; the number of nodes will only change within this range. For fault-tolerant operation of system components, we recommend using at least two working nodes in the cluster. Nodes can be located in different groups.
Click Save.

Configure Cluster Autoscaler

You can configure Cluster Autoscaler separately for each node group.

Parameters, their descriptions, and default values can be found in the Cluster Autoscaler parameters table. If you do not specify a parameter in the manifest, the default value will be used.

Example manifest:

apiVersion: v1
kind: ConfigMap
metadata:
    name: cluster-autoscaler-nodegroup-options
    namespace: kube-system
data:
    config.yaml: |
        150da0a9-6ea6-4148-892b-965282e195b0:
          scaleDownUtilizationThreshold: 0.55
          scaleDownUnneededTime: 7m
          zeroOrMaxNodeScaling: true
        e3dc24ca-df9d-429c-bcd5-be85f8d28710:
          scaleDownGpuUtilizationThreshold: 0.25
          ignoreDaemonSetsUtilization: true

Here, 150da0a9-6ea6-4148-892b-965282e195b0 and e3dc24ca-df9d-429c-bcd5-be85f8d28710 are unique identifiers (UUIDs) of node groups in the cluster. You can view them in the control panel: in the top menu, click Products ⟶ Managed Kubernetes ⟶ Kubernetes section ⟶ cluster page ⟶ copy the UUID above the node group card, next to the pool segment.

Cluster Autoscaler parameters

	Description	Default value
scaleDownUtilizationThreshold	Minimum vCPU and RAM utilization of a node at which the system can delete the node. If a node uses less than the specified percentage of vCPU and RAM, for example less than 50% with a value of `0.5`, the system deletes the node	0.5
scaleDownGpuUtilizationThreshold	Minimum GPU utilization at which the system can delete a node. If a node uses less than the specified percentage of GPU, for example less than 50% with a value of `0.5`, the system deletes the node	0.5
scaleDownUnneededTime	Waiting time before deleting a low-load node. The system does not delete a node immediately when the node load drops — it waits for the specified time to ensure that the load reduction is stable	10m
scaleDownUnreadyTime	Waiting time before deleting a node in the `NotReady` status. The system will not leave a node in the `NotReady` status in the cluster — it will wait for the specified time to make sure the node is frozen and will not recover, and then delete it	20m
maxNodeProvisionTime	Waiting time for adding a new node. If an error occurs and the node is not added within the specified time, the system will restart the node addition process	15m
zeroOrMaxNodeScaling	Allows the number of nodes to be automatically changed only to zero or the maximum you have set. Useful if you need the system to deploy all nodes in a group at once when load appears, and remove all nodes when there is no load	false
ignoreDaemonSetsUtilization	Allows ignoring daemon services (DaemonSets) when the system determines whether to decrease the number of nodes in a group. If the value is `true`, daemon services are ignored	false

Autoscaling using Karpenter

Operating principle

Karpenter is a cluster autoscaling tool with flexible settings. Unlike Cluster Autoscaler, Karpenter does not just use existing node groups; it can also create new node groups.

Karpenter can select the optimal node configuration—taking into account not only technical parameters but also cost. Karpenter selects the cheapest option that fits the current load. This is possible because Karpenter integrates directly with the OpenStack API, which is used to create cloud platform resources.

If the cluster is in the ACTIVE status, Karpenter checks whether there are pods (Pod) in the PENDING status and analyzes the load — requests from pods for vCPU, RAM, and GPU. Depending on the check results, node groups and nodes are added or deleted. Karpenter can only delete nodes and node groups that it created.

During this time, the cluster enters the PENDING_SCALE_UP or PENDING_SCALE_DOWN status. The cluster status during autoscaling is ACTIVE. For more information about cluster statuses, see the View cluster status guide.

Install Karpenter

In the control panel, in the top menu, click Products and select Managed Kubernetes.
In the Clusters section, open the cluster page → Settings tab.
Click Download kubeconfig. Downloading the kubeconfig file is unavailable if the cluster status is PENDING_CREATE, PENDING_ROTATE_CERTS, PENDING_DELETE, or ERROR.
Export the path to the kubeconfig file to the KUBECONFIG environment variable:
```
export KUBECONFIG=<path>
```
Specify <path> — path to the cluster_name.yaml kubeconfig file, where <cluster_name> is the cluster name.
Export the Managed Kubernetes cluster ID to the ClusterID environment variable:
```
export ClusterID=<cluster_id>
```
Specify <cluster_id> — Managed Kubernetes cluster ID, which can be viewed in the control panel: in the top menu, click Products → Managed Kubernetes → cluster page → copy the ID under the cluster name, next to the region and pool.
Install Karpenter using Helm:
```
helm install karpenter-helmrelease oci://ghcr.io/selectel/mks-charts/karpenter: \
--namespace kube-system \
--set controller.settings.clusterID=$ClusterID
```
By default, the latest available version is installed. Available versions can be found in the mks-charts repository of Selectel documentation on GitHub.

Configure Karpenter

To configure autoscaling using Karpenter, set up NodePool and NodeClass objects.

NodePool describes the rules for selecting and scaling nodes. For example:

what types of nodes can be created;
with what configurations (flavors) and resources;
when these nodes can be deleted or recreated.

Each NodePool refers to a specific NodeClass. In configurations (flavors) with network volumes, the NodeClass defines the infrastructure parameters of the network volumes to be used by the nodes in the cluster. A single cluster can have several different NodeClasses, such as those that differ in type or size of network volume. Configurations (flavors) with a local boot disk can also be used, but the local disk parameters are determined by the selected configuration.

Learn more about NodePool in the NodePools article of Karpenter documentation.

Check cluster compliance with requirements.
Create a NodeClass.
Create a NodePool.

1. Check cluster compliance with requirements

Make sure that the Kubernetes version is 1.28 or higher. You can upgrade the cluster version.
Ensure that there is at least one node in the cluster with at least 2 vCPU and 4 GiB of RAM. For optimal Karpenter performance, we recommend adding two nodes to the cluster, each with at least 2 vCPU and 4 GiB of RAM;
Make sure that autoscaling is disabled.
Make sure that auto-healing is disabled.

2. Create a NodeClass

Create a nodeclass.yaml YAML file with the manifest for the NodeClass object.

Example of a NodeClass manifest for a network volume of type Universal:
```
apiVersion: karpenter.k8s.selectel/v1alpha1
kind: SelectelNodeClass
metadata:
    name: default
spec:
    disk:
        categories:
            - universal
        sizeGiB: 30
```
Where:
- universal — network volume type;
- 30 — network volume size in GB.
Apply the manifest:
```
kubectl apply -f nodeclass.yaml
```

3. Create a NodePool

Create a nodepool.yaml YAML file with the manifest for the NodePool object. For descriptions of all parameters except for the parameters of the requirements block, see the NodePools guide in Karpenter documentation. For descriptions of the parameters of the requirements block, see the Requirements block parameters in NodePool table.

Example of a NodePool manifest when using the Calico network plugin

apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
    name: default
spec:
    template:
        spec:
            nodeClassRef:
                name: default
                kind: SelectelNodeClass
                group: karpenter.k8s.selectel
            requirements:
                - key: topology.kubernetes.io/zone
                  operator: In
                  values: ['ru-7a', 'ru-7b']
                - key: node.kubernetes.io/instance-type
                  operator: In
                  values: ['SL1.1-2048', 'SL1.2-4096', 'SL1.2-8192']
                - key: karpenter.sh/capacity-type
                  operator: In
                  values: ['on-demand']
            expireAfter: 720h
    disruption:
        consolidationPolicy: WhenEmptyOrUnderutilized
        consolidateAfter: 0s
    limits:
        cpu: '1000'
        memory: 1000Gi

Example of a NodePool manifest when using the Cilium network plugin

apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
    name: default
spec:
    template:
        spec:
            nodeClassRef:
                name: default
                kind: SelectelNodeClass
                group: karpenter.k8s.selectel
            startupTaints:
                - key: node.cilium.io/agent-not-ready
                  value: "true"
                  effect: NoExecute
                - key: node.cilium.io/agent-not-ready
                  effect: NoSchedule
            requirements:
                - key: topology.kubernetes.io/zone
                  operator: In
                  values: ['ru-7a', 'ru-7b']
                - key: node.kubernetes.io/instance-type
                  operator: In
                  values: ['SL1.1-2048', 'SL1.2-4096', 'SL1.2-8192']
                - key: karpenter.sh/capacity-type
                  operator: In
                  values: ['on-demand']
            expireAfter: 720h
    disruption:
        consolidationPolicy: WhenEmptyOrUnderutilized
        consolidateAfter: 0s
    limits:
        cpu: '1000'
        memory: 1000Gi

Apply the manifest:
```
kubectl apply -f nodepool.yaml
```

Parameters of the requirements block in NodePool

In the NodePool object, the requirements block describes the requirements for the nodes to be created.

Parameter	Description
karpenter.sh/capacity-type	Type of nodes being created: `on-demand` — non-preemptible nodes; or `spot` — preemptible nodes
karpenter.k8s.selectel/instance-category	Configuration line. For example, Standard Line (`SL`) or GPU Line (`GL`). Learn more about configurations in the Configurations guide
karpenter.k8s.selectel/instance-family	Configuration line and line generation. For example, `SL1` or `GL1`
karpenter.k8s.selectel/instance-generation	Line generation. For example, `1` or `2`
node.kubernetes.io/instance-type	Configurations (flavors). For example, `["SL1.1-2048", "SL1.2-4096", "SL1.2-8192"]`. Configurations can be found in the List of fixed configuration flavors in all pools subsection of the Configurations guide
karpenter.k8s.selectel/instance-cpu	Number of vCPUs. For example, `Gt: "4"` — more than 4
karpenter.k8s.selectel/instance-memory	RAM size in GiB. For example, `Gt: "8"` — more than 8 GiB
karpenter.k8s.selectel/instance-gpu-manufacturer	GPU manufacturer. Available value — `["NVIDIA"]`
karpenter.k8s.selectel/instance-gpu-name	GPU name for configurations with GPU. For example, `["A100", "H100"]`. Available GPUs can be found in the Available GPUs subsection of the Create a Managed Kubernetes cluster with GPU guide
karpenter.k8s.selectel/instance-gpu-count	Number of GPUs. For example, `Gt: "0"` — more than one GPU
karpenter.k8s.selectel/instance-local-disk	Indicates whether a local disk of the cloud platform is used as a boot drive. Available values are `["true"]` and `["false"]`
topology.kubernetes.io/zone	Pool segments where node groups can be created. For example, `["ru-7a", "ru-7b"]`. Available values can be found in the Managed Kubernetes subsection of the Product availability by location guide