Automating Kubernetes Workload Rightsizing With StormForge

As Kubernetes workloads grow in complexity, ensuring optimal resource utilization while maintaining performance becomes a significant challenge. Over-provisioning leads to wasted costs, while under-provisioning can degrade application performance. StormForge offers a machine learning-driven approach to automate workload rightsizing, helping teams strike the perfect balance between cost and performance. 

This article provides a comprehensive guide to implementing StormForge for Kubernetes workload optimization.

Prerequisites

Before getting started, ensure you have a working Kubernetes cluster (using tools like minikube, kind, or managed services like RKS, GKE, EKS, or AKS). You’ll also need Helm, kubectl, and the StormForge CLI installed, along with an active StormForge account. A monitoring solution like Prometheus is recommended but optional.

Set Up Your Environment

Ensure Kubernetes Cluster Access

Have a working Kubernetes cluster (e.g., Minikube, Kind, GKE, EKS, or AKS).

Confirm cluster connectivity:

kubectl get nodes

Install Helm

Verify Helm installation:

helm version

Install Helm if needed by following Helm Installation Instructions.

Deploy a Sample Application

Use a simple example application, such as Nginx:

kubectl apply -f https://k8s.io/examples/application/deployment.yaml

Confirm the application is running:

kubectl get pods

Install the StormForge CLI

Download and install the StormForge CLI:

curl -fsSL https://downloads.stormforge.io/install | bash

Authenticate the CLI with your StormForge account:

stormforge login

Deploy the StormForge Agent

Use the StormForge CLI to initialize your Kubernetes cluster:

stormforge init

Verify that the StormForge agent is deployed:

kubectl get pods -n stormforge-system

Create a StormForge Experiment

Define an experiment YAML file (e.g., experiment.yaml):

apiVersion: optimize.stormforge.io/v1

kind: Experiment

metadata:

  name: nginx-optimization

spec:

  target:

    deployments:

    - name: nginx-deployment

      containers:

      - name: nginx

        requests:

          cpu: "100m"

          memory: "128Mi"

        limits:

          cpu: "500m"

          memory: "256Mi"

Apply the experiment configuration:

stormforge apply -f experiment.yaml

Run the Optimization Process

Start the optimization:

stormforge optimize run nginx-optimization

Monitor the progress of the optimization using the CLI or StormForge dashboard.

Review and Apply Recommendations

Once the optimization is complete, retrieve the recommendations:

stormforge optimize recommendations nginx-optimization

Update your Kubernetes deployment manifests with the recommended settings:

requests:

  cpu: "200m"

  memory: "160Mi"

limits:

  cpu: "400m"

  memory: "240Mi"

Apply the updated configuration:

kubectl apply -f updated-deployment.yaml

Validate the Changes

Confirm that the deployment is running with the updated settings:

kubectl get pods

Monitor resource utilization to verify the improvements:

kubectl top pods

Integrate with Monitoring Tools (Optional)

If Prometheus is not installed, you can install it for additional metrics:

helm install prometheus prometheus-community/prometheus

Use Prometheus metrics for deeper insights into resource usage and performance.

Automate for Continuous Optimization

Set up a recurring optimization schedule using CI/CD pipelines. Then, regularly review recommendations as application workloads evolve.

Conclusion

StormForge provides an efficient and automated solution for optimizing Kubernetes workloads by leveraging machine learning to balance performance and resource utilization. By following the step-by-step guide, you can easily integrate StormForge into your Kubernetes environment, deploy experiments, and apply data-driven recommendations to rightsize your applications. 

This process minimizes costs by eliminating resource wastage and ensures consistent application performance. Integrating StormForge into your DevOps workflows enables continuous optimization, allowing your teams to focus on innovation while maintaining efficient and reliable Kubernetes operations.