Autoscaling in Kubernetes with Vertical Pod Autoscaler (VPA)

Overview

For a Kubernetes-based e-commerce application that experiences variable workloads throughout the day. During peak hours, such as flash sales or high-traffic periods, the application encounters increased demand, leading to performance challenges. To address this, you implement Vertical Pod Autoscaler (VPA) to optimize resource allocations for improved application responsiveness dynamically. Vertical Pod Autoscaler is invaluable in addressing the challenges of variable workloads in an e-commerce application. By dynamically optimizing resource allocations, VPA ensures optimal performance, enhances user experience, and contributes to cost-efficient resource utilization.

The blog goes through the significance of Vertical Pod Autoscaler (VPA) in Kubernetes, emphasizing its role in dynamically optimizing resource allocations for individual pods. Key features, benefits, and practical use cases, such as enhancing performance in an e-commerce application during variable workloads, are explored.

Introduction

Kubernetes stands as a leader in container orchestration, offering robust features to manage and scale containerized applications.

Prerequisites

• Kubernetes Cluster
• Installation of Kubernetes Metrics Server (Refer to the doc for Deploying the Kubernetes Metrics Server on a Cluster Using Kubectl (oracle.com))

Vertical Pod Autoscaler (VPA)

The Vertical Pod Autoscaler is a sophisticated component within the Kubernetes ecosystem, designed to dynamically adjust resource requests for individual pod containers based on their real-time usage metrics. Unlike its counterpart, the Horizontal Pod Autoscaler (HPA), which scales the number of pod replicas, VPA focuses on fine-tuning CPU and memory resource allocations for individual pods, optimizing resource utilization.

Key Features and Benefits

• Efficient Resource Utilization
• Improved Application Performance
• Cost Optimization
• Adaptability to Workload Changes

Steps to Implement Vertical Pod Autoscaler

Step 1: Install VPA in Your Cluster

Download the source code for the Vertical Pod Autoscaler from GitHub. For instance, use the following command:

Navigate to the directory for the Vertical Pod Autoscaler:

If you have previously deployed the Vertical Pod Autoscaler, remove it with the command:

Deploy the Vertical Pod Autoscaler using the command:

Confirm the successful creation of Vertical Pod Autoscaler pods by running the command:

Step 2: Launch the Sample Application

To deploy a deployment and a corresponding Vertical Pod Autoscaler, initiate the deployment of the sample hamster application using the following command:

This deployment of the hamster application generates a deployment featuring two pods and a Vertical Pod Autoscaler linked to the deployment.

Confirm the successful creation of the hamster pods by executing the command:

Inspect the CPU and memory reservations using the kubectl describe pod command and one of the hamster pod names obtained in the preceding step. For instance:

In the configuration file you got by the above command and in the “requests” section of the output, observe the existing CPU and memory allocations for the pod as shown below:

The Recommender component of the Vertical Pod Autoscaler assesses pod behavior to evaluate the adequacy of CPU and memory reservations. It’s important to note that the observed CPU and memory reservations may differ.

The current reservations are insufficient due to a deliberate under-resourcing of the sample hamster application. Each pod runs a single container with the following specifications:

• CPU request is set at 100 milliCores, but the container attempts to use more than 500 milliCores.
• Memory reservation is notably lower than the required amount for proper execution.

Step 3: Monitor the Scaling Operation

After evaluating the inadequacy of CPU and memory reservations in the original pods of the sample hamster application, the Vertical Pod Autoscaler, specifically the Updater, initiates the relaunch of pods with revised values recommended by the Recommender. It’s important to note that the Vertical Pod Autoscaler does not modify the deployment template but updates the actual requests of the pods.

To track the pods in the sample hamster application and await the initiation of a new hamster pod with a different name by the Updater, use the following command:

Once a new hamster pod has started, inspect its CPU and memory reservations using the kubectl describe pod command along with the pod’s name, as shown in this example:

In the “Requests” section of the output, observe the CPU and memory reservations for the new pod:

In the given example, notice the increased CPU reservation to 587 milliCores and the memory reservation to 262,144 Kilobytes. The original pod had insufficient resources, and the Vertical Pod Autoscaler has adjusted the reservations to more suitable values. It’s important to acknowledge that CPU and memory reservations may vary.

Step 4: Inspection

Inspect the recommendations provided by the Vertical Pod Autoscaler, specifically those made by the Recommender, using the following command:

Step 5: Cleanup

To uninstall the sample application, execute the following command:

In the directory for the Vertical Pod Autoscaler (vertical-pod-autoscaler), eliminate the Vertical Pod Autoscaler deployment with the command:

Conclusion

Vertical Pod Autoscaler emerges as a pivotal tool within the Kubernetes ecosystem, offering a sophisticated approach to resource optimization. By dynamically adjusting the resource requests of individual pods based on their actual usage, VPA addresses the challenges of efficient resource utilization, cost optimization, and adaptability to changing workloads. As organizations delve into the realm of autoscaling in Kubernetes, the integration of Vertical Pod Autoscaler stands out as a strategic choice to elevate the efficiency of containerized workloads and ensure the optimal performance of applications.

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