image-processing-cluster

Overview

This project demonstrates a scalable image processing application that applies various filters to images, designed for deployment in a Kubernetes cluster. It features built-in auto-scaling based on CPU load, ensuring efficient resource usage under varying workloads.

Key Features

• Image Filtering Service: Supports multiple filters such as grayscale, blur, sharpen, and contour using Pillow.
• CPU-Intensive Task Handling: Simulates CPU-intensive computations to trigger Kubernetes Horizontal Pod Autoscaling (HPA).
• Automatic Scaling: Configured to dynamically adjust the number of pods based on CPU utilization, ensuring performance under heavy workloads.
• Kubernetes Deployment: Fully containerized using Docker and deployed on Kubernetes, leveraging both Deployment and HPA configurations.
• Load Balancing: Traffic to the application is managed by a Kubernetes LoadBalancer for high availability.

Architecture

The architecture consists of a Flask-based service running in a container that performs image filtering tasks and CPU-intensive computations. The system is optimized to auto-scale when CPU load exceeds the configured threshold, ensuring optimal performance under stress.

Technologies Used

• Python (Flask, Pillow)
• Docker for containerization
• Kubernetes for orchestration
• Horizontal Pod Autoscaler for scaling based on CPU usage

Running Locally

To run the project locally:

1. Build the Docker image:

   docker build -t flask-app:latest .

2. Deploy to Kubernetes:

   kubectl apply -f deploy.yaml

Testing

Use the test.py script to verify image processing and CPU load generation. The system will automatically scale based on CPU usage, demonstrating the efficiency of the Kubernetes HPA.