⚙️ Distributed Load Balancer Simulation

A Python-based distributed system that simulates how a central load balancer distributes workloads to multiple worker nodes based on their real-time CPU usage.
This project demonstrates the fundamentals of distributed computing, socket programming, and resource-aware task allocation.

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Overview

The system consists of:

• Master Node (Load Balancer): Accepts connections from workers, receives their CPU load, and assigns tasks to the least-loaded node.
• Worker Nodes: Connect to the master, send their CPU usage (using psutil), and execute tasks when assigned.

This simulation provides insight into how real-world load balancers like Nginx or Kubernetes schedulers handle distributed workloads efficiently.

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Tech Stack

Technology	Purpose
Python 3	Core programming language
Sockets	Network communication between nodes
psutil	CPU usage and system metrics
Linux	Environment for running multiple terminals

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Key Features

Real-time CPU monitoring using psutil
Dynamic task assignment based on load
Supports multiple worker connections
Simple and interactive distributed simulation
Cross-terminal setup for a realistic multi-node experience

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Project Structure

distributed-load-balancer/ │
├── master.py # Load balancer (server)
├── worker.py # Worker node (client)
├── README.md # Documentation

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How to Run

1️⃣ Start the Master Node

Open Terminal 1 and run:

python3 master.py

2️⃣ Start One or More Worker Nodes

In Terminal 2, 3, ..., run:

python3 worker.py

3️⃣ Observe the Simulation

Workers send their CPU load to the master node. The master assigns tasks to the least loaded worker. Workers display when they receive and complete tasks

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output