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Exercise - Enable cluster scalability on AKS

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Enable the cluster autoscaler on an AKS cluster

1. Open the Azure Cloud Shell in your browser and select Bash.

2. Create an Azure resource group using the az group create command.

   az group create --name myResourceGroup --location eastus
   

3. Create a new AKS cluster with the cluster autoscaler enabled using the az aks create command and the --enable-cluster-autoscaler flag.

   az aks create --resource-group myResourceGroup --name myAKSCluster --enable-addons monitoring --enable-msi-auth-for-monitoring --enable-cluster-autoscaler --min-count 1 --max-count 10 --generate-ssh-keys
   

   It takes a few minutes to create the cluster.

4. Connect to your cluster using the az aks get-credentials command.

   az aks get-credentials --resource-group myResourceGroup --name myAKSCluster
   

5. View the nodes in your cluster using the kubectl get nodes command.

   kubectl get nodes
   

   Your output should look similar to the following example output:

   NAME                                STATUS   ROLES   AGE   VERSION
   aks-nodepool1-12345678-vmss000000   Ready    agent   1m    v1.26.6
   aks-nodepool1-12345678-vmss000001   Ready    agent   1m    v1.26.6
   aks-nodepool1-12345678-vmss000002   Ready    agent   1m    v1.26.6
   

Deploy the sample application

1. In Cloud Shell, create a manifest file for the Kubernetes Deployment called deployment.yml using the touch command.

   touch deployment.yml
   

2. Open the manifest file using the code command.

   code deployment.yml
   

3. Paste the following code into the manifest file.

   apiVersion: apps/v1
   kind: Deployment
   metadata:
     name: contoso-website
   spec:
     replicas: 35
     selector:
       matchLabels:
         app: contoso-website
     template:
       metadata:
         labels:
           app: contoso-website
       spec:
         containers:
           - image: mcr.microsoft.com/mslearn/samples/contoso-website
             name: contoso-website
             resources:
               requests:
                 cpu: 100m
                 memory: 128Mi
               limits:
                 cpu: 250m
                 memory: 256Mi
             ports:
               - containerPort: 80
                 name: http
   

4. Save the file and close the editor.

Update the cluster autoscaler profile

You can fine-tune the autoscaler profiles by setting a series of flags in the configuration. View the list of available flags in Use the cluster autoscaler profile. For this example, you update the autoscaler to reduce the polling time to check for pending pods and reduce the amount of time it needs to wait before scaling down from a previous state.

1. Update the cluster autoscaler profile using the az aks update command with the --cluster-autoscaler-profile flag.

   az aks update --resource-group myResourceGroup --name myAKSCluster --cluster-autoscaler-profile scan-interval=5s scale-down-unready-time=5m scale-down-delay-after-add=5m
   

2. Scale down the deployment using the kubectl scale deployment command.

   kubectl scale deployment contoso-website --replicas 5
   

3. Check the cluster autoscaler logs and query for the cluster-autoscaler-status config map using the kubectl describe cm command.

   kubectl describe cm cluster-autoscaler-status -n kube-system
   

   Before the scale down, your output should look similar to the following example output:

   Cluster-autoscaler status at 2023-11-09 20:08:14.892961701 +0000 UTC:
   Cluster-wide:
     Health:      Healthy (ready=3 unready=0 notStarted=0 longNotStarted=0 registered=3 longUnregistered=0)
                  LastProbeTime:      2023-11-09 19:56:57.890988498 +0000 UTC m=+1673.465985892
                  LastTransitionTime: 2023-11-09 19:45:09.593593337 +0000 UTC m=+774.168590731
     ScaleUp:     NoActivity (ready=3 registered=3)
                  LastProbeTime:      2023-11-09 19:56:57.890988498 +0000 UTC m=+1673.465985892
                  LastTransitionTime: 2023-11-09 19:45:09.593593337 +0000 UTC m=+774.168590731
     ScaleDown:   CandidatesPresent (candidates=3)
                  LastProbeTime:      2023-11-09 19:56:57.890988498 +0000 UTC m=+1673.465985892
                  LastTransitionTime: 2023-11-09 19:56:52.440038763 +0000 UTC m=+1101.015036157
   

4. Wait about five minutes for the autoscaler to complete the scale down, and then rerun the previous kubectl describe cm.

   After the scale down, your output should look similar to the following example output:

   Cluster-autoscaler status at 2023-11-09 20:14:39.123206413 +0000 UTC:
   Cluster-wide:
     Health:      Healthy (ready=1 unready=0 (resourceUnready=0) notStarted=0 longNotStarted=0 registered=1 longUnregistered=0)
                  LastProbeTime:      2023-11-09 20:14:39.113206413 +0000 UTC m=+2150.697175601
                  LastTransitionTime: 2023-11-09 19:45:09.593593337 +0000 UTC m=+774.168590731
     ScaleUp:     NoActivity (ready=1 registered=1)
                  LastProbeTime:      2023-11-09 20:14:39.113206413 +0000 UTC m=+2150.697175601
                  LastTransitionTime: 2023-11-09 19:45:09.593593337 +0000 UTC m=+774.168590731
     ScaleDown:   NoCandidates (candidates=0)
                  LastProbeTime:      2023-11-09 20:14:39.113206413 +0000 UTC m=+2150.697175601
                  LastTransitionTime: 2023-11-09 20:07:08.79828656 +0000 UTC m=+1718.924760896
   

5. View the nodes in your cluster using the kubectl get nodes command.

   kubectl get nodes
   

   Your output should look similar to the following example output, with the number of nodes reduced to one:

   NAME                                STATUS   ROLES   AGE   VERSION
   aks-nodepool1-12345678-vmss000000   Ready    agent   37m    v1.26.6
   

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