EdgeWise Resource Manager

EdgeWise Resource Manager is a dynamic resource allocation tool designed for edge computing devices. It monitors resource usage in real-time and adjusts resource allocation based on predefined policies to ensure optimal performance and efficiency.

Features

• Real-time monitoring of CPU, memory, and disk usage.
• Dynamic resource allocation based on predefined policies.
• Containerized deployment using Docker.
• Orchestration with Kubernetes.

Table of Contents

• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Building and Pushing Docker Images
  • Deploying to Kubernetes
• Repository Structure
• Contributing
• License

Getting Started

Prerequisites

Before you begin, ensure you have the following installed:

• Docker
• Kubernetes
• Python 3.8+
• kubectl (Kubernetes command-line tool)

Installation

1. Clone the repository:

   git clone https://github.com/abhaypawar/edgewise.git
   cd edgewise
   ./deploy.sh

2. Deploy to Kubernetes:

kubectl apply -f k8s-deployment.yaml

Usage

Building and Pushing Docker Images

The deploy.sh script handles the building and pushing of Docker images to Docker Hub. Ensure you replace your-dockerhub-username in the deploy.sh script and the Kubernetes deployment file with your actual Docker Hub username.

#!/bin/bash
# deploy.sh

# Build Docker images
docker build -t edge-monitor -f Dockerfile.monitor .
docker build -t resource-allocator -f Dockerfile.allocator .

# Push Docker images to a registry (Docker Hub in this case)
docker tag edge-monitor your-dockerhub-username/edge-monitor
docker tag resource-allocator your-dockerhub-username/resource-allocator

docker push your-dockerhub-username/edge-monitor
docker push your-dockerhub-username/resource-allocator

# Deploy to Kubernetes
kubectl apply -f k8s-deployment.yaml

Deploying to Kubernetes

Apply the Kubernetes deployment file to set up the EdgeWise Resource Manager on your cluster.

# k8s-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: edge-computing-manager
spec:
  replicas: 1
  selector:
    matchLabels:
      app: edge-computing-manager
  template:
    metadata:
      labels:
        app: edge-computing-manager
    spec:
      containers:
      - name: monitor
        image: your-dockerhub-username/edge-monitor
        volumeMounts:
        - mountPath: /app/metrics.json
          name: metrics-volume
      - name: allocator
        image: your-dockerhub-username/resource-allocator
        volumeMounts:
        - mountPath: /app/metrics.json
          name: metrics-volume
      volumes:
      - name: metrics-volume
        emptyDir: {}

To deploy, run:

kubectl apply -f k8s-deployment.yaml

Repository Structure

edgewise-resource-manager/
    monitor.py: Script to monitor CPU, memory, and disk usage.
    allocate_resources.py: Script to allocate resources based on monitored metrics.
    Dockerfile.monitor: Dockerfile for the monitoring module.
    Dockerfile.allocator: Dockerfile for the resource allocation module.
    deploy.sh: Bash script to build, tag, and push Docker images, and deploy to Kubernetes.
    k8s-deployment.yaml: Kubernetes deployment configuration.

Contributing

We welcome contributions! Please follow these steps:

Fork the repository.
Create a new branch (git checkout -b feature-branch).
Commit your changes (git commit -am 'Add new feature').
Push to the branch (git push origin feature-branch).
Create a new Pull Request.

License

This project is licensed under the Apache 2.0 License. See the LICENSE file for details.

Additional Notes:

1. Replace Placeholders: Ensure you replace your-github-username and your-dockerhub-username with your actual GitHub and Docker Hub usernames.
2. Monitoring Script: Adjust the time.sleep(10) interval in monitor.py if needed for more or less frequent monitoring.
3. Resource Allocation Policy: Update the resource allocation logic in allocate_resources.py to match your specific requirements or policies.

This updated README provides a complete overview and detailed instructions to set up and use the EdgeWise Resource Manager.