Scheduling Algorithm Simulator

This project simulates various CPU scheduling algorithms and generates statistics and visualizations based on their performance. The code consists of three main components:

1. Scheduler: simulator.c simulates the execution of processes using different scheduling algorithms.
2. Generator: generator.py generates random data for the scheduling simulation.
3. Statistics Processor: stats.py processes the generated data, calculates statistics, and creates visualizations.

Code Structure

The project contains the following files:

• simulator.c: Simulates the execution of processes using different scheduling algorithms.
• generator.py: Generates random process data and exports it to CSV files.
• stats.py: Reads the generated CSV files, calculates statistics, and creates visualizations.
• execution.csv: Sample CSV input file (you may generate your own data using generator.py).
• out/: Directory where all generated graphs and statistics will be saved.

How to Set Up

1. Install Dependencies: Ensure you have Python installed (version 3.6 or later). Then, install the required libraries:

   pip install numpy scipy matplotlib

2. Generate Input Data: Run generator.py to generate random data for different process sizes. The data will be saved in the inputs directory.

   python generator.py

3. Run Statistics and Graph Generation: After generating the data, run stats.py to read the CSV files, calculate statistics, and generate graphs.

   python stats.py

What Works

• Scheduling Simulation: The simulator.c file simulates the execution of processes using the following scheduling algorithms:

  • First-Come, First-Served (FCFS)
  • Shortest remaining time first (SRTF)
  • Priority Scheduling (PR)
  • Round Robin (RR)

• Data Generation: The generator.py file successfully generates random data including:

  • Process ID
  • Arrival Time
  • Execution Time
  • Random Priority

• Statistics Calculation: The stats.py file calculates:

  • Average waiting time
  • Standard deviation of waiting time
  • Median and 90th percentile of waiting time
  • Total context switches and total runtime

• Graph Generation: Various visualizations are generated:

  • Box plot for waiting time vs turnaround time
  • Histogram for waiting time distribution
  • Bar plots for context switches per process
  • Grouped bar plots for waiting time and turnaround time comparisons
  • Comparison graphs for different scheduling algorithms across various process sizes

What Doesn't Work

• The project does not include a direct implementation of CPU scheduling algorithms; it requires pre-generated data from generator.py.
• Error handling is minimal; if the CSV format does not match expectations, the code may raise errors.

Output

The generated graphs will be saved in the out directory, and the statistics will be exported to CSV files named according to the scheduling algorithms used and the number of processes.

Example Usage

Generate data for 100 processes:

python generator.py

Process and visualize the data:

python stats.py

Contributions

Feel free to contribute by submitting issues or pull requests to enhance the functionality or improve the codebase.