Scaling Azure Kubernetes Service (AKS)

Scaling your Azure Kubernetes Service (AKS) cluster is crucial for ensuring your applications can handle varying loads efficiently and cost-effectively. AKS provides several mechanisms to scale both your applications (pods) and your underlying infrastructure (nodes).

Understanding Scaling in AKS

There are two primary types of scaling in AKS:

  • Pod Scaling: This involves adjusting the number of pod replicas for your deployments. When your application experiences increased traffic or demand, you can automatically scale up the number of pods to handle the load.
  • Node Scaling: This involves adjusting the number of virtual machines (nodes) in your AKS node pool. If your pods require more resources than available on the current nodes, or if you want to optimize costs, you can scale the node pool.

1. Pod Autoscaling

The Kubernetes Horizontal Pod Autoscaler (HPA) automatically scales the number of pods in a deployment based on observed CPU utilization or other select metrics.

Configuring Horizontal Pod Autoscaler (HPA)

You can define an HPA resource that targets your deployment. The HPA will then monitor the specified metrics and adjust the replicas field of your deployment accordingly.

Tip: Ensure your pods are configured with resource requests and limits for CPU and memory. The HPA relies on these values to make scaling decisions.

Here's an example of an HPA configuration:


apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  name: my-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: my-app-deployment # The name of your deployment
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 50

In this example, the HPA will try to maintain a CPU utilization of 50% across all pods. If the utilization exceeds this, it will increase the number of replicas up to 10. If it drops below, it will decrease the replicas down to 2.

2. Node Scaling

The Cluster Autoscaler is an AKS feature that automatically adjusts the number of nodes in your node pool. It works by integrating with Kubernetes' autoscaling mechanisms.

How the Cluster Autoscaler Works

The Cluster Autoscaler watches for pods that are failing to schedule due to insufficient resources. If it detects such pods, and scaling up the node pool would allow them to be scheduled, it will provision new nodes. Conversely, if nodes are underutilized for a sustained period and their pods can be rescheduled onto other nodes, the Cluster Autoscaler will remove those underutilized nodes.

Enabling and Configuring the Cluster Autoscaler

The Cluster Autoscaler can be enabled during AKS cluster creation or updated on an existing cluster. You specify the minimum and maximum number of nodes for each node pool.

Example using Azure CLI to enable Cluster Autoscaler:


az aks nodepool update \
    --resource-group myResourceGroup \
    --cluster-name myAKSCluster \
    --name agentpool \
    --enable-cluster-autoscaler \
    --min-count 1 \
    --max-count 5

This command enables the Cluster Autoscaler for the `agentpool` node pool in `myAKSCluster`, setting the minimum node count to 1 and the maximum to 5.

3. Vertical Pod Autoscaler (VPA)

While HPA scales the number of pods, Vertical Pod Autoscaler (VPA) adjusts the resource requests (CPU and memory) for your pods. This can help ensure your pods have appropriate resource allocations, which in turn can improve the efficiency of HPA and the Cluster Autoscaler.

VPA is typically deployed as an add-on and works by recommending or automatically applying updated resource requests to your pods. It can operate in different modes:

  • Off: VPA only provides recommendations.
  • Initial: VPA applies recommendations only when a pod is created.
  • Recreate: VPA evicts and recreates pods with updated resource requests.
  • Auto: VPA evicts and recreates pods with updated resource requests, and also applies them to pods that are already running.

Best Practices for Scaling

  • Start with HPA: For most application workloads, start by configuring HPA based on CPU or memory utilization.
  • Set Realistic Limits: Define sensible minReplicas and maxReplicas for both HPA and Cluster Autoscaler.
  • Monitor Performance: Regularly monitor your cluster's performance, resource utilization, and scaling events to fine-tune your configurations.
  • Consider Custom Metrics: For more complex scenarios, explore using custom metrics with HPA (e.g., queue lengths, request latency).
  • Understand Node Costs: Be mindful of the costs associated with scaling out your node pools.

Effective scaling strategies are key to a robust and responsive AKS environment. By leveraging HPA and the Cluster Autoscaler, you can ensure your applications meet demand while optimizing resource usage.