K8s Cluster Autoscaler

Guide to Kubernetes Autoscaling
calendar October 11, 2021

Kubernetes Cluster Autoscaler is one of the most popular automation tools for Kubernetes hardware capacity management. It is supported by the major cloud platforms and can streamline the Kubernetes (K8s) cluster management process.

To help you get started with Kubernetes Cluster Autoscaler, in this article, we’ll explain how it handles capacity management by adding and removing worker nodes automatically, its requirements, and provide examples of how to use it in practice.

Cluster AutoScaling Overview

As new pods are deployed and replica counts for existing pods increase, cluster worker nodes can use up all their allocated resources. As a result, no more pods can be scheduled on existing workers. Some pods can go into a pending state, waiting for CPU and memory and possibly creating an outage. As Kubernetes admin, you can manually solve this problem by adding more worker nodes to the cluster to enable scheduling of additional pods.

The problem is this manual process is time-consuming and scales poorly. Fortunately, Kubernetes Cluster Autoscaler can solve this problem by automating capacity management. Specifically, Cluster Autoscaler automates the process of adding and removing worker nodes from a K8s cluster.

Most cloud providers support Cluster Autoscaling, but it’s not supported for on-prem self-hosted K8s environments. Cluster Autoscaling is a “cloud-only” feature because on-prem deployments lack the APIs for automatic virtual machine creation and deletion required for the autoscaling process.

By default, Cluster Autoscaler is installed on most K8s cloud installations. If Cluster Autoscaler isn’t installed in your cloud environment but is supported, you can manually install it.

Cluster AutoScaling Requirements and Supported Platforms

Support for Cluster Autoscaler is available on these Kubernetes platforms:

  • Google Kubernetes Engine (GKE)
  • Azure Kubernetes (AKS)
  • Elastic Kubernetes Service (EKS)

as well as several other less popular K8s cloud platforms.

Each cloud provider has its own implementation of Cluster Autoscaler with different limitations.

You can enable Cluster Autoscaler during cluster creation using a platform-specific GUI or CLI method. For example, on GKE the command below enables Cluster Autoscaler on a multi-zone cluster with a one-node per zone minimum and four-node per zone maximum:

gcloud container clusters create example-cluster 
    --num-nodes 2 
    --zone us-central1-a 
    --node-locations us-central1-a,us-central1-b,us-central1-f 
    --enable-autoscaling --min-nodes 1 --max-nodes 4

How Cluster Autoscaling Works

Kubernetes scheduler dynamically places pods on worker nodes using a best-effort QoS strategy. For Cluster Autoscaler to work as expected and applications to get the underlying host resources they need, resource requests and limits must be defined on pods. Without resource requests and limits, Cluster Autoscaler can’t make accurate decisions.

Cluster AutoScaler periodically checks the status of nodes and pods and takes action based on node utilization or pod scheduling status. When Cluster Autoscaler detects pending pods on the cluster, it will add more nodes until pending pods are scheduled or the cluster reaches the max node limit. Cluster Autoscaler will remove extra nodes if node utilization is low and pods can move to other nodes.

The steps in cluster autoscaling
The steps in cluster autoscaling

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How to Use Cluster AutoScaler

Now that we understand how Cluster Autoscaler works, we can get started using it in practice. This section will walk through an example application deployment on Google Cloud Platform using Google Kubernetes Engine (GKE) with Cluster Autoscaler enabled with resource requests and limits defined.

We’ll scale the application by increasing the number of replicas until the autoscaler detects the pending pods. Then we’ll see the autoscaler events with a new node added. Finally, we will scale down the replicas for the autoscaler to remove the extra nodes.

To begin, create a demo cluster with 3 worker nodes

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ gcloud container clusters create scaling-demo --num-nodes=3 
  NAME          LOCATION       MASTER_VERSION  MASTER_IP       MACHINE_TYPE  NODE_VERSION    NUM_NODES  STATUS
  scaling-demo  us-central1-a  1.20.8-gke.900  35.225.137.158  e2-medium     1.20.8-gke.900  3          RUNNING

Enable autoscaling on the cluster

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ gcloud beta container clusters update scaling-demo --enable-autoscaling --min-nodes 1 --max-nodes 5
  Updating scaling-demo...done.Updated [https://container.googleapis.com/v1beta1/projects/qwiklabs-gcp-03-a94f05d7b8a0/zones/us-central1-a/clusters/scaling-demo].

Get the number of initial nodes created

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get nodes
  NAME                                          STATUS   ROLES    AGE   VERSION
  gke-scaling-demo-default-pool-b182e404-5l2v   Ready    <none>   6m    v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-87gq   Ready    <none>   6m    v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-kwfc   Ready    <none>   6m    v1.20.8-gke.900

Create the example deployment application with resource requests and limits defined so that Kubernetes Scheduler can allocate pods on nodes with required capacity and Cluster Autoscaler can allocate more nodes when needed.

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ cat deployment.yaml
  apiVersion: v1
  kind: Service
  metadata:
    name: application-cpu
    labels:
      app: application-cpu
  spec:
    type: ClusterIP
    selector:
      app: application-cpu
    ports:
      - protocol: TCP
        name: http
        port: 80
        targetPort: 80
  ---
  apiVersion: apps/v1
  kind: Deployment
  metadata:
    name: application-cpu
    labels:
      app: application-cpu
  spec:
    selector:
      matchLabels:
        app: application-cpu
    replicas: 1
    strategy:
      type: RollingUpdate
      rollingUpdate:
        maxSurge: 1
        maxUnavailable: 0
    template:
      metadata:
        labels:
          app: application-cpu
      spec:
        containers:
        - name: application-cpu
          image: aimvector/application-cpu:v1.0.2
          imagePullPolicy: Always
          ports:
          - containerPort: 80
          resources:
            requests:
              memory: "50Mi"
              cpu: "500m"
            limits:
              memory: "500Mi"
              cpu: "2000m"
  
  student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl create -f deployment.yaml
  service/application-cpu created
  deployment.apps/application-cpu created

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The deployment is up and it has one pod up and running now

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get pods
  NAME                               READY   STATUS    RESTARTS   AGE
  application-cpu-7879778795-8t8bn   1/1     Running   0          9s

Let’s scale by adding one more replica pod by setting number of replicas to two

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl scale deploy/application-cpu --replicas 2
  deployment.apps/application-cpu scaled
  
  student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get pods
  NAME                               READY   STATUS    RESTARTS   AGE
  application-cpu-7879778795-8t8bn   1/1     Running   0          2m29s
  application-cpu-7879778795-rzxc7   0/1     Pending   0          5s

The new pod is pending due to insufficient resources. The Cluster Autoscaler will now take action. It detects the event and starts creating and adding a new worker node to the cluster.

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get events
  LAST SEEN   TYPE      REASON                    OBJECT                                             MESSAGE
  3m24s       Normal    Scheduled                 pod/application-cpu-7879778795-8t8bn               Successfully assigned default/application-cpu-7879778795-8t8bn to gke-scaling-demo-default-pool-b182e404-87gq
  3m23s       Normal    Pulling                   pod/application-cpu-7879778795-8t8bn               Pulling image "aimvector/application-cpu:v1.0.2"
  3m20s       Normal    Pulled                    pod/application-cpu-7879778795-8t8bn               Successfully pulled image "aimvector/application-cpu:v1.0.2" in 3.035424763s
  3m20s       Normal    Created                   pod/application-cpu-7879778795-8t8bn               Created container application-cpu
  3m20s       Normal    Started                   pod/application-cpu-7879778795-8t8bn               Started container application-cpu
  60s         Warning   FailedScheduling          pod/application-cpu-7879778795-rzxc7               0/3 nodes are available: 3 Insufficient cpu.
  56s         Normal    TriggeredScaleUp          pod/application-cpu-7879778795-rzxc7               pod triggered scale-up: [{https://www.googleapis.com/compute/v1/projects/qwiklabs-gcp-03-a94f05d7b8a0/zones/us-central1-a/instanceGroups/gke-scaling-demo-default-pool-b182e404-grp 3->4 (max: 5)}]
  2s          Warning   FailedScheduling          pod/application-cpu-7879778795-rzxc7               0/4 nodes are available: 1 node(s) had taint {node.kubernetes.io/not-ready: }, that the pod didn't tolerate, 3 Insufficient cpu.
  3m24s       Normal    SuccessfulCreate          replicaset/application-cpu-7879778795              Created pod: application-cpu-7879778795-8t8bn
  60s         Normal    SuccessfulCreate          replicaset/application-cpu-7879778795              Created pod: application-cpu-7879778795-rzxc7
  3m24s       Normal    ScalingReplicaSet         deployment/application-cpu                         Scaled up replica set application-cpu-7879778795 to 1
  60s         Normal    ScalingReplicaSet         deployment/application-cpu                         Scaled up replica set application-cpu-7879778795 to 2
  5m50s       Normal    RegisteredNode            node/gke-scaling-demo-default-pool-b182e404-5l2v   Node gke-scaling-demo-default-pool-b182e404-5l2v event: Registered Node gke-scaling-demo-default-pool-b182e404-5l2v in Controller
  5m50s       Normal    RegisteredNode            node/gke-scaling-demo-default-pool-b182e404-87gq   Node gke-scaling-demo-default-pool-b182e404-87gq event: Registered Node gke-scaling-demo-default-pool-b182e404-87gq in Controller
  13s         Normal    Starting                  node/gke-scaling-demo-default-pool-b182e404-ccft   Starting kubelet.
  13s         Warning   InvalidDiskCapacity       node/gke-scaling-demo-default-pool-b182e404-ccft   invalid capacity 0 on image filesystem
  12s         Normal    NodeHasSufficientMemory   node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeHasSufficientMemory
  12s         Normal    NodeHasNoDiskPressure     node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeHasNoDiskPressure
  12s         Normal    NodeHasSufficientPID      node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeHasSufficientPID
  12s         Normal    NodeAllocatableEnforced   node/gke-scaling-demo-default-pool-b182e404-ccft   Updated Node Allocatable limit across pods
  10s         Normal    RegisteredNode            node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft event: Registered Node gke-scaling-demo-default-pool-b182e404-ccft in Controller
  9s          Normal    Starting                  node/gke-scaling-demo-default-pool-b182e404-ccft   Starting kube-proxy.
  6s          Warning   ContainerdStart           node/gke-scaling-demo-default-pool-b182e404-ccft   Starting containerd container runtime...
  6s          Warning   DockerStart               node/gke-scaling-demo-default-pool-b182e404-ccft   Starting Docker Application Container Engine...
  6s          Warning   KubeletStart              node/gke-scaling-demo-default-pool-b182e404-ccft   Started Kubernetes kubelet.
  6s          Warning   NodeSysctlChange          node/gke-scaling-demo-default-pool-b182e404-ccft   {"unmanaged": {"net.netfilter.nf_conntrack_buckets": "32768"}}
  1s          Normal    NodeReady                 node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeReady

Let’s check if a new worker was added by Cluster Autoscaler

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get nodes
  NAME                                          STATUS   ROLES    AGE   VERSION
  gke-scaling-demo-default-pool-b182e404-5l2v   Ready    <none>   13m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-87gq   Ready    <none>   13m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-ccft   Ready    <none>   83s   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-kwfc   Ready    <none>   13m   v1.20.8-gke.900

The pod is now allocated and running

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get pods
  NAME                               READY   STATUS    RESTARTS   AGE
  application-cpu-7879778795-8t8bn   1/1     Running   0          4m45s
  application-cpu-7879778795-rzxc7   1/1     Running   0          2m21s

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Let’s scale up by adding one more pod

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl scale deploy/application-cpu --replicas 3
  deployment.apps/application-cpu scaled

Again, a new Pod goes to pending state due to insufficient resources

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get pods
  NAME                               READY   STATUS    RESTARTS   AGE
  application-cpu-7879778795-56l6d   0/1     Pending   0          16s
  application-cpu-7879778795-8t8bn   1/1     Running   0          5m22s
  application-cpu-7879778795-rzxc7   1/1     Running   0          2m58s

We can see the events related to new node creation

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get events
  LAST SEEN   TYPE      REASON                    OBJECT                                             MESSAGE
  33s         Warning   FailedScheduling          pod/application-cpu-7879778795-56l6d               0/4 nodes are available: 4 Insufficient cpu.
  29s         Normal    TriggeredScaleUp          pod/application-cpu-7879778795-56l6d               pod triggered scale-up: [{https://www.googleapis.com/compute/v1/projects/qwiklabs-gcp-03-a94f05d7b8a0/zones/us-central1-a/instanceGroups/gke-scaling-demo-default-pool-b182e404-grp 4->5 (max: 5)}]
  5m39s       Normal    Scheduled                 pod/application-cpu-7879778795-8t8bn               Successfully assigned default/application-cpu-7879778795-8t8bn to gke-scaling-demo-default-pool-b182e404-87gq
  5m38s       Normal    Pulling                   pod/application-cpu-7879778795-8t8bn               Pulling image "aimvector/application-cpu:v1.0.2"
  5m35s       Normal    Pulled                    pod/application-cpu-7879778795-8t8bn               Successfully pulled image "aimvector/application-cpu:v1.0.2" in 3.035424763s
  5m35s       Normal    Created                   pod/application-cpu-7879778795-8t8bn               Created container application-cpu
  5m35s       Normal    Started                   pod/application-cpu-7879778795-8t8bn               Started container application-cpu
  3m15s       Warning   FailedScheduling          pod/application-cpu-7879778795-rzxc7               0/3 nodes are available: 3 Insufficient cpu.
  3m11s       Normal    TriggeredScaleUp          pod/application-cpu-7879778795-rzxc7               pod triggered scale-up: [{https://www.googleapis.com/compute/v1/projects/qwiklabs-gcp-03-a94f05d7b8a0/zones/us-central1-a/instanceGroups/gke-scaling-demo-default-pool-b182e404-grp 3->4 (max: 5)}]
  2m17s       Warning   FailedScheduling          pod/application-cpu-7879778795-rzxc7               0/4 nodes are available: 1 node(s) had taint {node.kubernetes.io/not-ready: }, that the pod didn't tolerate, 3 Insufficient cpu.
  2m7s        Normal    Scheduled                 pod/application-cpu-7879778795-rzxc7               Successfully assigned default/application-cpu-7879778795-rzxc7 to gke-scaling-demo-default-pool-b182e404-ccft
  2m6s        Normal    Pulling                   pod/application-cpu-7879778795-rzxc7               Pulling image "aimvector/application-cpu:v1.0.2"
  2m5s        Normal    Pulled                    pod/application-cpu-7879778795-rzxc7               Successfully pulled image "aimvector/application-cpu:v1.0.2" in 1.493155878s
  2m5s        Normal    Created                   pod/application-cpu-7879778795-rzxc7               Created container application-cpu
  2m5s        Normal    Started                   pod/application-cpu-7879778795-rzxc7               Started container application-cpu
  5m39s       Normal    SuccessfulCreate          replicaset/application-cpu-7879778795              Created pod: application-cpu-7879778795-8t8bn
  3m15s       Normal    SuccessfulCreate          replicaset/application-cpu-7879778795              Created pod: application-cpu-7879778795-rzxc7
  33s         Normal    SuccessfulCreate          replicaset/application-cpu-7879778795              Created pod: application-cpu-7879778795-56l6d
  5m39s       Normal    ScalingReplicaSet         deployment/application-cpu                         Scaled up replica set application-cpu-7879778795 to 1
  3m15s       Normal    ScalingReplicaSet         deployment/application-cpu                         Scaled up replica set application-cpu-7879778795 to 2
  33s         Normal    ScalingReplicaSet         deployment/application-cpu                         Scaled up replica set application-cpu-7879778795 to 3
  2m28s       Normal    Starting                  node/gke-scaling-demo-default-pool-b182e404-ccft   Starting kubelet.
  2m28s       Warning   InvalidDiskCapacity       node/gke-scaling-demo-default-pool-b182e404-ccft   invalid capacity 0 on image filesystem
  2m27s       Normal    NodeHasSufficientMemory   node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeHasSufficientMemory
  2m27s       Normal    NodeHasNoDiskPressure     node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeHasNoDiskPressure
  2m27s       Normal    NodeHasSufficientPID      node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeHasSufficientPID
  2m27s       Normal    NodeAllocatableEnforced   node/gke-scaling-demo-default-pool-b182e404-ccft   Updated Node Allocatable limit across pods
  2m25s       Normal    RegisteredNode            node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft event: Registered Node gke-scaling-demo-default-pool-b182e404-ccft in Controller
  2m24s       Normal    Starting                  node/gke-scaling-demo-default-pool-b182e404-ccft   Starting kube-proxy.
  2m21s       Warning   ContainerdStart           node/gke-scaling-demo-default-pool-b182e404-ccft   Starting containerd container runtime...
  2m21s       Warning   DockerStart               node/gke-scaling-demo-default-pool-b182e404-ccft   Starting Docker Application Container Engine...
  2m21s       Warning   KubeletStart              node/gke-scaling-demo-default-pool-b182e404-ccft   Started Kubernetes kubelet.
  2m21s       Warning   NodeSysctlChange          node/gke-scaling-demo-default-pool-b182e404-ccft   {"unmanaged": {"net.netfilter.nf_conntrack_buckets": "32768"}}
  2m16s       Normal    NodeReady                 node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeReady

Verify that Cluster Autoscaler creates and adds new worker node in response to pending pod event

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get nodes
  
  NAME                                          STATUS   ROLES    AGE     VERSION
  gke-scaling-demo-default-pool-b182e404-5l2v   Ready    <none>   14m     v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-7sg8   Ready    <none>   2s      v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-87gq   Ready    <none>   14m     v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-ccft   Ready    <none>   2m43s   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-kwfc   Ready    <none>   14m     v1.20.8-gke.900

We can see that a new pod is allocated and scheduled successfully

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get pods
  
  NAME                               READY   STATUS    RESTARTS   AGE
  application-cpu-7879778795-56l6d   1/1     Running   0          82s
  application-cpu-7879778795-8t8bn   1/1     Running   0          6m28s
  application-cpu-7879778795-rzxc7   1/1     Running   0          4m4s

Now let’s scale the pods back down to one. Scaling down will remove extra pods and trigger Cluster Autoscaler to remove the extra nodes created earlier.

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl scale deploy/application-cpu --replicas 1
  deployment.apps/application-cpu scaled
  
  
  student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get pods
  
  NAME                               READY   STATUS    RESTARTS   AGE
  application-cpu-7879778795-8t8bn   1/1     Running   0          7m41s

Get the number of nodes , they are still not scaled down yet , as the autoscaler takes time to detect unused nodes

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get nodes
  
  NAME                                          STATUS   ROLES    AGE   VERSION
  gke-scaling-demo-default-pool-b182e404-5l2v   Ready    <none>   25m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-7sg8   Ready    <none>   10m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-87gq   Ready    <none>   25m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-ccft   Ready    <none>   13m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-kwfc   Ready    <none>   25m   v1.20.8-gke.900

Let’s wait for a few minutes (5–15 minutes) and see the scale down events related to node removal

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get events
  LAST SEEN   TYPE      REASON                                                                                                      OBJECT                                             MESSAGE
  
  9m31s       Normal    ScaleDown                                                                                                   node/gke-scaling-demo-default-pool-b182e404-7sg8   node removed by cluster autoscaler
  8m53s       Normal    NodeNotReady                                                                                                node/gke-scaling-demo-default-pool-b182e404-7sg8   Node gke-scaling-demo-default-pool-b182e404-7sg8 status is now: NodeNotReady
  7m44s       Normal    Deleting node gke-scaling-demo-default-pool-b182e404-7sg8 because it does not exist in the cloud provider   node/gke-scaling-demo-default-pool-b182e404-7sg8   Node gke-scaling-demo-default-pool-b182e404-7sg8 event: DeletingNode
  7m42s       Normal    RemovingNode                                                                                                node/gke-scaling-demo-default-pool-b182e404-7sg8   Node gke-scaling-demo-default-pool-b182e404-7sg8 event: Removing Node gke-scaling-demo-default-pool-b182e404-7sg8 from Controller
  9m30s       Normal    ScaleDown                                                                                                   node/gke-scaling-demo-default-pool-b182e404-ccft   node removed by cluster autoscaler
  8m42s       Normal    NodeNotReady                                                                                                node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft status is now: NodeNotReady
  7m38s       Normal    Deleting node gke-scaling-demo-default-pool-b182e404-ccft because it does not exist in the cloud provider   node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft event: DeletingNode
  7m37s       Normal    RemovingNode                                                                                                node/gke-scaling-demo-default-pool-b182e404-ccft   Node gke-scaling-demo-default-pool-b182e404-ccft event: Removing Node gke-scaling-demo-default-pool-b182e404-ccft from Controller

Check the number of nodes, latest update

student_02_c444e24e3915@cloudshell:~ (qwiklabs-gcp-03-a94f05d7b8a0)$ kubectl get nodes
  NAME                                          STATUS   ROLES    AGE   VERSION
  gke-scaling-demo-default-pool-b182e404-5l2v   Ready    <none>   34m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-87gq   Ready    <none>   34m   v1.20.8-gke.900
  gke-scaling-demo-default-pool-b182e404-kwfc   Ready    <none>   34m   v1.20.8-gke.900

Now we see that the number of nodes is scaled back down to the initial number of nodes in the cluster.

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Cluster Autoscaler Limitations

Cluster Autoscaler is a helpful tool but has limitations. This section summarizes its main shortcomings and points to commercial tools that can help you overcome them.

  • Cluster Autoscaler is not supported on on-premise environments until an autoscaler is implemented for on-premise deployments.
  • Scaling up is not immediate. Therefore, a pod will be in a pending state for a few minutes until a new worker is added.
  • Some cluster workers may have other dependencies, such as local volumes bindings from other pods. As a result, a node may be a candidate for removal but can’t be removed by Cluster Autoscaler.
  • Cluster Autoscaler works based on resource requests, not actual usage. This fact can lead to mis-allocating nodes if resource requests and limits are not properly calculated and set. This issue is critical because you can waste resources in your cluster under the false impression that autoscaling is addressing them. Supplemental tools can help analyze the effective efficiency throughout the cluster (container, pod, namespace, node) to avoid pockets of waste due to misconfiguration.
  • Cluster Autoscaler adds additional nodes but administrators are responsible for defining the right size for each node. The same commercial tools that help with optimizing the node size while also identifying the cluster’s wasted capacity due to unused resource requests

These limitations will impact most K8s deployments, especially at scale. You can learn more about container infrastructure optimization, designed to enable intelligent scaling for complex environments.

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