Leveraging the Cluster Autoscaler to Optimize Kubernetes Costs on Azure

As organizations continue to embrace Kubernetes for their cloud-native applications, managing and optimizing the cost of running these environments becomes a critical concern. One of the key tools in the Kubernetes cost optimization arsenal is the Cluster Autoscaler (CA), a powerful component that can dynamically scale the number of nodes in your Azure Kubernetes Service (AKS) cluster to match the changing demands of your workloads.

In this article, we’ll dive deep into the Cluster Autoscaler, exploring how it works, how to enable and configure it in your AKS environment, and the best practices for leveraging it to achieve maximum cost savings.

Understanding the Cluster Autoscaler

The Cluster Autoscaler is a Kubernetes component that continuously monitors the resource utilization of your AKS cluster and automatically adjusts the number of nodes to meet the demands of your running workloads. It does this by watching for pending pods that can’t be scheduled due to resource constraints, as well as identifying nodes that are underutilized and can be removed from the cluster.

When the Cluster Autoscaler detects a need to scale up, it will provision additional nodes to the cluster, ensuring that there are enough resources available to run all the requested pods. Conversely, when it identifies nodes that are underutilized and no longer needed, it will remove those nodes, reducing the overall cost of running your Kubernetes environment.

Enabling the Cluster Autoscaler in AKS

To get started with the Cluster Autoscaler in your AKS environment, you can follow these steps:

  1. Create a new AKS cluster with the Cluster Autoscaler enabled: When creating a new AKS cluster, you can enable the Cluster Autoscaler by using the --enable-cluster-autoscaler parameter and specifying the minimum and maximum number of nodes.
az aks create 
  --resource-group myResourceGroup 
  --name myAKSCluster 
  --node-count 1 
  --vm-set-type VirtualMachineScaleSets 
  --load-balancer-sku standard 
  --enable-cluster-autoscaler 
  --min-count 1 
  --max-count 3 
  --generate-ssh-keys
  1. Enable the Cluster Autoscaler on an existing AKS cluster: If you have an existing AKS cluster, you can enable the Cluster Autoscaler using the az aks update command and the --enable-cluster-autoscaler parameter.
az aks update 
  --resource-group myResourceGroup 
  --name myAKSCluster 
  --enable-cluster-autoscaler 
  --min-count 1 
  --max-count 3

Configuring the Cluster Autoscaler

Once the Cluster Autoscaler is enabled, you can further fine-tune its behavior by adjusting various configuration settings. These settings allow you to customize the Cluster Autoscaler’s scaling behavior to better suit your specific workload requirements and cost optimization goals.

Some of the key configuration options include:

  • Scaling Interval: Adjust the frequency at which the Cluster Autoscaler checks for scaling events, from the default of 10 seconds.
  • Scaling Delay: Control the delay between scaling events, to prevent rapid, unnecessary scaling.
  • Node Utilization Threshold: Set the threshold for node utilization, below which the Cluster Autoscaler will consider removing nodes.
  • Maximum Graceful Termination Time: Specify the maximum time the Cluster Autoscaler should wait for pods to be gracefully terminated before removing a node.

You can update these settings using the az aks update command and the --cluster-autoscaler-profile parameter, like this:

az aks update 
  --resource-group myResourceGroup 
  --name myAKSCluster 
  --cluster-autoscaler-profile scan-interval=30s,scale-down-delay-after-add=10m,scale-down-unneeded-time=3m

Best Practices for Cluster Autoscaler Usage

To get the most out of the Cluster Autoscaler and ensure optimal cost savings, consider the following best practices:

  1. Combine with the Horizontal Pod Autoscaler: Use the Horizontal Pod Autoscaler (HPA) in conjunction with the Cluster Autoscaler to achieve a more comprehensive scaling solution. The HPA will scale your application pods up and down based on metrics like CPU and memory utilization, while the Cluster Autoscaler adjusts the underlying node count to accommodate the changing pod demands.

  2. Monitor Cluster Autoscaler Logs and Events: Regularly review the Cluster Autoscaler’s logs and Kubernetes events to ensure it is operating as expected and to identify any potential issues or scaling bottlenecks.

  3. Optimize VM Sizes: Carefully select the appropriate VM sizes for your AKS node pools to ensure that you’re not over-provisioning resources and paying for more than you need.

  4. Enable Cluster Autoscaler on Multiple Node Pools: If your AKS cluster has multiple node pools, enable the Cluster Autoscaler on each one to ensure that scaling decisions are made at the individual pool level, rather than across the entire cluster.

By following these best practices and leveraging the powerful capabilities of the Cluster Autoscaler, you can significantly optimize the costs of running Kubernetes on Azure, while maintaining the scalability and performance your applications require.

For more information and resources on the Cluster Autoscaler in AKS, be sure to check out the official Microsoft Learn article.