Memory-Partitioning-and-Process-Scheduling-in-Python

memory management with fixed partition technique in two single queue and multiple queue method.

Overview

This repository contains a Python implementation of memory partitioning and process scheduling. The code demonstrates two methods for allocating processes to memory partitions: single queue and multiple queue. By using threading and queues, the code efficiently manages process allocation and deallocation, aiming to optimize memory utilization and throughput.

Features

• Dynamic Memory Partitioning: The memory is partitioned dynamically based on the given size.
• Process Scheduling: Two methods of scheduling (single queue and multiple queue) are implemented.
• Threading: Utilizes Python's threading module to handle process allocation and deallocation concurrently.
• Performance Metrics: Calculates memory utilization and throughput for both scheduling methods.
• Colorful Output: Uses the colorama library to enhance console output for better readability.

Requirements

• Python 3.x
• colorama library

You can install the colorama library using pip:

pip install colorama

Usage

Clone the repository:

git clone https://github.com/yourusername/memory-partitioning-scheduling.git
cd memory-partitioning-scheduling

Follow the prompts: Enter the total memory size. Enter the number of processes. Enter the memory size and duration time for each process.

Example

Contribution

Feel free to submit issues or pull requests. For major changes, please open an issue first to discuss what you would like to change.

License

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