Distributed-Task-Scheduling-System

A Java-based distributed system for matrix operations and image convolution processing, implementing a client-server architecture with slave nodes for parallel computation.

Feature

• Distributed Computing: Server distributes tasks among slave nodes for parallel processing
• Matrix Operations:
  • Addition
  • Subtraction
  • Multiplication
  • Image Convolution
• Real-time Processing: Handles multiple client requests simultaneously
• Error Handling: Robust validation for matrix dimensions and operations
• Socket-based Communication: TCP/IP communication between components

Architecture

The system consists of three main components: Client: Submits matrix operations and receives results Server: Manages client connections and distributes tasks Slaves: Perform actual computations (matrix operations and convolution)   Client <-> Server <-> Slaves

Technical Stack

• Language: Java
• Communication: TCP/IP Sockets
• Threading: Multi-threading for parallel processing
• Data Structures: 2D Arrays for matrix operations
• Image Processing: Custom convolution implementation

Installation

1.Clone the repository

git clone https://github.com/kawtarkar/Distributed-Task-Scheduling-System

2.Compile the Java files

javac Server.java 
javac Client.java
javac Slave.java

3.Start the components in order:

java Server
java Slave # Run multiple instances for parallel processing
java Client

Usage

Matrix Operations

• 1.Start the server
• 2.Launch slave nodes
• 3.Connect with client
• 4.Choose operation type:
  • 1: Addition
  • 2: Multiplication
  • 3: Subtraction
  • 5: Convolution
• 6.Input matrices according to prompts
• 7.Receive computed results

Future Enhancements

• Add support for more matrix operations
• Implement load balancing between slaves
• Add GUI interface for client
• Improve error handling and recovery
• Add support for larger matrices