AWS-EKS-Monitoring-App

Overview

This project demonstrates the end-to-end deployment of a cloud-native monitoring application on AWS EKS. It showcases expertise in Kubernetes, Docker, AWS IAM, ECR, and troubleshooting real-world deployment challenges. The application monitors system resources using Python and Flask, providing a visual representation of CPU and memory usage.

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Tech Stack

• Cloud Provider: AWS (IAM, EKS, ECR)
• Containerization: Docker
• Orchestration: Kubernetes (kubectl, manifests)
• Backend: Python, Flask
• Monitoring: psutil (CPU & memory usage)
• Automation: AWS CLI, Boto3

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Key Features

✅ Cloud-Native Deployment: Fully deployed on AWS EKS using Kubernetes manifests.
✅ Secure IAM Configuration: Least-privilege AWS IAM setup for secure access.
✅ Dockerized Application: Efficient containerization and registry management with AWS ECR.
✅ Optimized Node Group: Cost-effective Kubernetes cluster provisioning.
✅ CI/CD Ready: Potential integration with GitHub Actions for automated deployments.
✅ Troubleshooting & Debugging: Hands-on experience in diagnosing and resolving Kubernetes issues.

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Deployment Steps

1️⃣ AWS IAM Configuration

1. Created an IAM User: Configured necessary permissions for AWS services.
2. Configured IAM Locally:

   aws configure

3. Verified IAM Setup:

   aws iam list-users

2️⃣ Environment Setup

1. Installed Kubernetes CLI:

   sudo apt install -y kubectl

2. Developed Python Monitoring App:
   • Uses psutil to track CPU & memory.
   • Serves metrics using Flask (render_template for index.html).

3️⃣ Dockerizing the Application

1. Created Dockerfile (with dependency management and exposed port 5000).
2. Built and Ran Docker Container:

   docker build -t monitoring-app .
   docker run -p 5000:5000 monitoring-app

3. Resolved Docker Issues:
   • Fixed incorrect port binding warnings.

4️⃣ AWS ECR Setup

1. Created an ECR Repository Using Boto3:

   import boto3
   client = boto3.client('ecr')
   response = client.create_repository(repositoryName='monitoring-app')
   print(response)

2. Pushed Docker Image to ECR:

   aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin <aws-account-id>.dkr.ecr.us-east-1.amazonaws.com
   docker tag monitoring-app:latest <aws-account-id>.dkr.ecr.us-east-1.amazonaws.com/monitoring-app:latest
   docker push <aws-account-id>.dkr.ecr.us-east-1.amazonaws.com/monitoring-app:latest

5️⃣ Kubernetes Deployment on AWS EKS

1. Created an EKS Cluster:

   eksctl create cluster --name cloud-native-cluster --region us-east-1

2. Configured Node Group (Optimized Cost by Using t2.micro)
3. Deployed Kubernetes Manifest Files:

   kubectl apply -f deployment.yaml

4. Exposed the Application:

   kubectl expose deployment monitoring-app --type=LoadBalancer --port=80 --target-port=5000

5. Updated Kubernetes Cluster Config:

   aws eks update-kubeconfig --name cloud-native-cluster

6️⃣ Troubleshooting & Debugging

1. Checked Service Status:

   kubectl get svc

2. Debugged Failures Using kubectl describe:

   kubectl describe pod <pod-name>

3. Forwarded Service for External Access:

   kubectl port-forward svc/monitoring-app 8080:5000

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Outcome

🚀 Successfully deployed a cloud-native monitoring application on AWS Kubernetes, accessible via port forwarding.
📌 Demonstrated proficiency in DevOps tools, cloud deployment, container orchestration, and troubleshooting real-world infrastructure issues.
🛠️ Open for improvements—potential to integrate CI/CD pipelines for automated deployment!

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License

This project is licensed under the MIT License - see the LICENSE file for details.