Introduction to Kubernetes Autoscaling

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February 19, 2024

Key Takeaways

Kubernetes has emerged as a powerful solution for managing containerized applications, and one of its key features is autoscaling. But what is Kubernetes autoscaling, you ask? In a nutshell, it's an intelligent system that adjusts the number of pods (the smallest deployable units in Kubernetes) or the resources allocated to them based on the current workload. This means your application can automatically scale up when demand increases and scale down when demand drops, optimising resource usage and minimising costs.

Before we dive into the details of how Kubernetes autoscaling works, let's take a step back and appreciate the bigger picture. When you develop a modern application, you need it to be responsive, resilient, and adaptable to varying loads. This is where containerization comes into play, allowing you to package your application and its dependencies into lightweight, portable units called containers. Kubernetes is the orchestration platform that manages these containers, ensuring they're running smoothly and efficiently.

Now that you've got a basic understanding of Kubernetes, let's circle back to autoscaling. There are several components involved in Kubernetes autoscaling, including the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler (CA). These components work together to make sure your application is using resources effectively and can handle changes in demand.

In this article, we'll take you on a journey through the world of Kubernetes autoscaling and resource allocation. We'll explore the basics of autoscaling, how to set up HPA, VPA, and CA, and share strategies for effective resource allocation. We'll also discuss best practices and common challenges you may encounter along the way.

So, buckle up and get ready to dive into the exciting realm of Kubernetes autoscaling! By the end of this article, you'll have a solid understanding of how to optimise your applications using this powerful feature, and you'll be well on your way to unlocking the full potential of Kubernetes.

Understanding the Basics of Kubernetes Autoscaling

Now that you have a glimpse of what Kubernetes autoscaling is all about, let's dive deeper into its key components and how they work together to help you optimise your applications. As mentioned earlier, the main components involved in Kubernetes autoscaling are the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and Cluster Autoscaler (CA).

Horizontal Pod Autoscaler (HPA)

The HPA is responsible for automatically adjusting the number of pods in a deployment or replica set based on the observed CPU usage or custom metrics. This means that if your application is experiencing a sudden spike in demand, the HPA can scale up the number of pods to handle the increased load. Conversely, if the demand drops, the HPA can scale down the number of pods, reducing resource consumption and cost.

The HPA operates by periodically checking the current resource usage against the target resource utilisation. If the observed utilisation deviates from the target, the HPA adjusts the number of replicas accordingly. You can also configure the HPA to scale based on custom metrics, giving you even more control over your application's scalability.

Vertical Pod Autoscaler (VPA)

While the HPA focuses on scaling the number of pods, the VPA is all about adjusting the resource limits for individual containers within a pod. This means that if a container is running out of memory or CPU, the VPA can automatically increase the resource limits, allowing the container to continue functioning without disruption.

The VPA operates by monitoring the resource usage of containers and comparing it to the current resource limits. If the observed usage is consistently higher or lower than the limits, the VPA recommends new resource limits for the containers. In some cases, the VPA can also automatically apply these recommendations, ensuring your application always has the right amount of resources.

Cluster Autoscaler (CA)

The CA takes autoscaling a step further by managing the number of nodes in your Kubernetes cluster. If your cluster is running out of resources due to increased demand, the CA can automatically add new nodes to the cluster. Similarly, if the demand drops and there are unused nodes, the CA can remove them, reducing infrastructure costs.

The CA monitors the overall resource usage in your cluster and compares it to the available capacity. If there are pending pods that cannot be scheduled due to insufficient resources, the CA adds new nodes to accommodate them. Likewise, if there are under-utilised nodes, the CA can remove them to save costs.

In summary, Kubernetes autoscaling is a powerful feature that helps you optimise your applications by automatically adjusting the number of pods, container resource limits, and nodes in your cluster based on the current workload. By combining the capabilities of the HPA, VPA, and CA, you can ensure that your application is always running efficiently and can adapt to changes in demand, providing a seamless experience for your users while minimizing costs. In the next section, we'll explore how to set up and configure these components to get the most out of Kubernetes autoscaling.

Setting up Kubernetes Autoscaling: HPA, VPA, and CA

Now that you've got a solid understanding of the basics of Kubernetes autoscaling, it's time to put that knowledge into practice by setting up the HPA, VPA, and CA for your applications. Let's explore each component in more detail and learn how to configure them effectively.

Horizontal Pod Autoscaler (HPA)

To set up the HPA for your application, you'll need to define a configuration file that specifies the target resource utilisation and the minimum and maximum number of replicas.

Here's an example configuration file:

apiVersion: autoscaling/v2beta2
kind: HorizontalPodAutoscaler
 name: sample-hpa
   apiVersion: apps/v1
   kind: Deployment
   name: sample-deployment
 minReplicas: 2
 maxReplicas: 5
 - type: Resource
     name: cpu
     targetAverageUtilization: 50

  • This configuration file creates a HorizontalPodAutoscaler (HPA) named "sample-hpa".
  • The HPA targets a Deployment named "sample-deployment" for scaling.
  • It specifies that the deployment should have a minimum of 2 replicas (minReplicas) and a maximum of 5 replicas (maxReplicas).
  • The autoscaler will monitor CPU utilization (resource: cpu) and aim for a target average utilization of 50% (targetAverageUtilization).

To deploy the HPA using kubectl, you can run the following command in your terminal:

kubectl apply -f sample-hpa.yaml

This command will apply the configuration defined in the "sample-hpa.yaml" file, creating the HorizontalPodAutoscaler in your Kubernetes cluster. Once deployed, the HPA will continuously monitor the CPU utilization of the pods in the specified deployment. If the average CPU utilization exceeds 50%, the HPA will automatically increase the number of replicas (up to a maximum of 5) to handle the increased load. Conversely, if the average CPU utilization decreases below 50%, the HPA will scale down the number of replicas (down to a minimum of 2) to save resources.

Vertical Pod Autoscaler (VPA)

To set up the VPA for your application, you'll need to install the VPA components in your cluster and create a VPA configuration file. First, install the VPA components using the following command:

kubectl apply -f

Next, create a VPA configuration file that specifies the target containers and the update policy. Here's an example configuration file:

kind: VerticalPodAutoscaler
 name: sample-vpa
   apiVersion: "apps/v1"
   kind: "Deployment"
   name: "sample-deployment"
   updateMode: "Auto"

  • This configuration file creates a VerticalPodAutoscaler (VPA) named "sample-vpa".
  • The VPA targets a Deployment named "sample-deployment" for vertical scaling.
  • updatePolicy specifies the policy for updating the pod resource requests.
  • updateMode: "Auto" indicates that the VPA should automatically adjust the resource requests based on pod usage.

To deploy the VPA using kubectl, you can run the following command in your terminal:

kubectl apply -f sample-vpa.yaml

This command will apply the configuration defined in the "sample-vpa.yaml" file, creating the VerticalPodAutoscaler in your Kubernetes cluster. Once deployed, the VPA will continuously monitor the resource usage of pods in the specified deployment and adjust their CPU and memory requests accordingly, optimising resource allocation for improved performance and efficiency.

Cluster Autoscaler (CA)

To set up the CA for your cluster, you'll need to install the CA components and configure your cluster's nodes to allow for autoscaling.

Create the YAML files:

apiVersion: v1
kind: ServiceAccount
 name: cluster-autoscaler
 namespace: kube-system


kind: ClusterRole
 name: cluster-autoscaler
- apiGroups: [""]
 resources: ["events"]
 verbs: ["create", "patch"]
- apiGroups: [""]
 resources: ["pods/eviction"]
 verbs: ["create"]
- apiGroups: [""]
 resources: ["pods/status"]
 verbs: ["update"]
- apiGroups: [""]
 resources: ["endpoints"]
 resourceNames: ["cluster-autoscaler"]
 verbs: ["get", "update"]


kind: ClusterRoleBinding
 name: cluster-autoscaler
 kind: ClusterRole
 name: cluster-autoscaler
- kind: ServiceAccount
 name: cluster-autoscaler
 namespace: kube-system


apiVersion: apps/v1
kind: Deployment
 name: cluster-autoscaler
 namespace: kube-system
 replicas: 1
     app: cluster-autoscaler
       app: cluster-autoscaler
     serviceAccountName: cluster-autoscaler
     - image:
       name: cluster-autoscaler
       - ./cluster-autoscaler
       - --v=4
       - --stderrthreshold=info
       - --cloud-provider=aws  # Change to your cloud provider if not AWS
       - --skip-nodes-with-local-storage=false
       - --balance-similar-node-groups=true
       - --skip-nodes-with-system-pods=false
       - --expander=random
       - --scale-down-unneeded-time=10m
       - --scale-down-delay-after-add=10m
       - --scale-down-delay-after-failure=3m
       - --scan-interval=10s
       - --max-surge=10

  • The provided YAML configuration sets up the necessary RBAC (Role-Based Access Control) rules and roles for the Cluster Autoscaler service account to function within the kube-system namespace.
  • It deploys the Cluster Autoscaler as a Deployment within the kube-system namespace, ensuring there is always one instance running.
  • The configuration specifies various parameters such as --cloud-provider, --scale-down-unneeded-time, and --scale-down-delay-after-failure to customise the behaviour of the Cluster Autoscaler based on your cluster environment and requirements.

Deploying Cluster Autoscaler:

Save the above YAML configuration to a file named cluster-autoscaler.yaml, then use kubectl to apply it:

kubectl apply -f cluster-autoscaler.yaml

This command will deploy the Cluster Autoscaler components to your Kubernetes cluster and configure them for autoscaling. Make sure to replace ${CLUSTER_NAME} with your actual cluster name before applying the configuration.

Now that you've set up the HPA, VPA, and CA for your Kubernetes applications, you're well on your way to optimising resource allocation and handling changes in demand efficiently. However, merely setting up autoscaling components is not enough – you also need to develop effective strategies for resource allocation and monitor your applications' performance. In the next section, we'll explore some tactics for efficient resource allocation and share best practices for Kubernetes autoscaling.

Strategies for Effective Resource Allocation in Kubernetes

While autoscaling is a powerful tool to optimise your applications, it's crucial to pair it with effective resource allocation strategies. By properly allocating resources, you can ensure that your applications perform well and minimise costs. Let's explore some strategies to effectively manage resources within your Kubernetes cluster.

Defining resource requests and limits is crucial for efficiently managing CPU and memory consumption by containers. Resource requests specify the minimum resources required for a container to operate, while limits define the maximum resources a container can utilise. By setting these parameters, you ensure that your applications have the necessary resources to run optimally without monopolising excessive resources.

Below is an example demonstrating how to define resource requests and limits within a Kubernetes deployment:

apiVersion: apps/v1
kind: Deployment
 name: my-deployment
     - name: my-container
       image: my-image
           cpu: 100m
           memory: 128Mi
           cpu: 200m
           memory: 256Mi

In this example, the container is configured with a resource request of 100m CPU and 128Mi memory, along with a resource limit of 200m CPU and 256Mi memory.

Utilising resource quotas is another method to manage resources within a namespace. Resource quotas enable you to define constraints on the total amount of resources that can be utilised within a namespace, preventing any single namespace from exhausting all available resources in the cluster and potentially affecting other applications.

To create a resource quota, you need to define a configuration file specifying the resource constraints for the namespace. Here's an example:

apiVersion: v1
kind: ResourceQuota
 name: my-quota
   requests.cpu: 1
   requests.memory: 1Gi
   limits.cpu: 2
   limits.memory: 2Gi

In this example, the resource quota imposes limits on the total amount of CPU and memory requests and limits within the namespace.

Monitoring and analysing resource usage are essential for making informed decisions regarding resource allocation and autoscaling. Kubernetes offers built-in tools like kubectl top and the Kubernetes Dashboard for monitoring resource usage. Additionally, third-party solutions such as Prometheus, Grafana, and Datadog provide more comprehensive monitoring and analysis capabilities, offering insights into application performance and resource consumption.

By implementing these strategies for resource allocation, you can ensure that your applications are using resources efficiently while maintaining high performance. Combine these tactics with the autoscaling components discussed earlier, and you'll be well on your way to optimising your Kubernetes applications. In the next section, we'll discuss best practices and common challenges in Kubernetes autoscaling and resource allocation.

Best Practices and Common Challenges in Kubernetes Autoscaling

Autoscaling in Kubernetes can be a game-changer for managing your applications, but it's essential to be aware of the best practices and common challenges you might encounter. By following best practices, you can ensure that your applications scale effectively, while being prepared for challenges will help you troubleshoot and optimise your autoscaling configuration. Let's dive into some key best practices and common challenges.

Best Practices:
  1. Set appropriate resource requests and limits: Make sure to define sensible resource requests and limits for your containers. This helps Kubernetes effectively schedule pods and ensures your applications have enough resources to perform well without consuming excessive resources.
  2. Use custom metrics: While the default CPU and memory metrics are useful, using custom metrics can provide better insights into your application's performance and scalability. This allows you to create more accurate scaling policies based on your application's specific needs.
  3. Monitor and adjust: Regularly monitor your application's performance and resource usage. Analyze the data to identify bottlenecks, inefficiencies, or potential cost savings. Adjust your autoscaling configurations and resource allocations as needed to continually optimise your applications.
  4. Test your autoscaling configurations: Before deploying your autoscaling configurations to production, test them in a staging environment. This will help you identify potential issues and fine-tune your configuration for optimal results.
Common Challenges:
  1. Overprovisioning or underprovisioning resources: One of the most common challenges is improperly allocating resources, leading to overprovisioning or underprovisioning. Overprovisioning can lead to increased costs, while underprovisioning can result in poor application performance. It's essential to strike the right balance by monitoring resource usage and adjusting allocations accordingly.
  2. Scaling latency: Autoscaling is not instantaneous, and there can be a delay between a change in demand and the scaling action. Be aware of this latency and plan for it in your application's design to ensure a seamless user experience.
  3. Scaling too frequently: Frequent scaling can lead to instability and increased costs. To prevent this, configure your autoscaling policies with appropriate thresholds, cooldown periods, or stabilisation windows to minimise unnecessary scaling actions.
  4. Complex autoscaling configurations: As you start using more advanced autoscaling features, such as custom metrics and multiple autoscaling components, the complexity of your configuration can increase. This can make it more challenging to manage and troubleshoot your autoscaling setup. Stay organised and document your configurations to help manage this complexity.

By following these best practices and being prepared for common challenges, you can ensure that your Kubernetes autoscaling setup is efficient, effective, and optimised for your applications. With a well-configured autoscaling system, you can enjoy the benefits of improved application performance, reduced infrastructure costs, and the ability to handle changes in demand with ease


Kubernetes autoscaling is a powerful tool for optimising your applications, enabling them to adapt to changes in demand and efficiently allocate resources. By leveraging the Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and Cluster Autoscaler, you can ensure your applications run smoothly and cost-effectively.

To get the most out of autoscaling, follow best practices for resource allocation, monitor your applications' performance, and be prepared for common challenges. By combining effective autoscaling strategies with proper resource allocation, you'll be well on your way to creating a highly optimized and scalable Kubernetes environment for your applications.

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