🖥️ CPU Scheduling Algorithm Simulator

An interactive, modern CPU Scheduling Algorithm Simulator built with Streamlit, featuring real-time visualizations, comprehensive performance metrics, and AI-powered explanations using Google's Gemini 1.5.

✨ Features

🎯 Core Features

1. 📊 Gantt Chart Visualization - Interactive timeline showing process execution with color-coded blocks
2. 🔬 Algorithm Comparison Mode - Run and compare all algorithms simultaneously with side-by-side analysis
3. 💾 Export Results (CSV/PDF) - Download simulation results in multiple formats for reporting
4. 🎬 Step-by-Step Animation - Animated execution timeline with play/pause controls
5. 📋 Predefined Scenarios - Quick-start templates (Gaming, Web Server, Batch Processing, Real-Time, Mixed)
6. 📈 Real-Time Metrics Dashboard - Live CPU utilization, throughput, and performance indicators
7. 🔄 Process Queue Visualization - Visual representation of ready queue states
8. 📊 Interactive Charts - Pie charts, line graphs, and bar charts for data analysis
9. 🤖 Gemini AI Chatbot - AI-powered advisor for algorithm explanations and recommendations
10. 🎚️ Performance Analysis Metrics - Comprehensive metrics including fairness index, response time, and efficiency

🎨 Design Features

• Modern Dark Theme with gradient accents and smooth animations
• Responsive Layout optimized for all screen sizes
• Custom CSS Styling with hover effects and transitions
• Theme Toggle (Dark/Light mode support)
• Professional UI with metric cards and data visualizations

🚀 Supported Algorithms

Algorithm	Description	Best For
FCFS	First Come First Serve	Simple queue-based systems
SJF	Shortest Job First	Minimizing waiting time
Priority	Priority-based scheduling	Critical process handling
Round Robin	Time-sliced scheduling	Fair CPU time distribution

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Step 1: Clone the Repository

git clone https://github.com/yourusername/cpu-scheduling-simulator.git
cd cpu-scheduling-simulator

Step 2: Create Virtual Environment (Recommended)

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Required Packages

streamlit>=1.28.0
pandas>=2.0.0
matplotlib>=3.7.0
plotly>=5.15.0
google-generativeai>=0.3.0
fpdf2>=2.7.0
numpy>=1.24.0
Pillow>=10.0.0

🎮 Usage

Running the Application

streamlit run app.py

The application will open in your default browser at http://localhost:8501

Quick Start Guide

1. Choose a Scenario (Optional)

   • Select from predefined scenarios in the sidebar
   • Or choose "Custom" to enter your own process data

2. Configure Processes

   • Set number of processes (1-10)
   • Enter Arrival Time (AT), Burst Time (BT), and Priority for each process
   • View process summary in the sidebar

3. Select Algorithm

   • Choose from FCFS, SJF, Priority, or Round Robin
   • For Round Robin, set the time quantum

4. Run Simulation

   • Click "▶️ Run Simulation" for single algorithm
   • Click "🔬 Compare All Algorithms" to compare all four

5. Explore Results

   • Overview Tab: Basic results and statistics
   • Gantt Chart Tab: Visual execution timeline
   • Metrics Dashboard: Comprehensive performance metrics
   • Detailed Analysis: Insights and interactive charts
   • Export Tab: Download results as CSV or PDF

6. Use AI Advisor

   • Switch to "🤖 AI Advisor" tab
   • Ask questions about algorithms
   • Get performance analysis and recommendations
   • Use quick action buttons for instant explanations

📊 Metrics Explained

Basic Metrics

• Waiting Time: Time spent waiting in ready queue
• Turnaround Time: Total time from arrival to completion
• Response Time: Time from arrival to first execution

Advanced Metrics

• CPU Utilization: Percentage of time CPU is busy
• Throughput: Processes completed per unit time
• Fairness Index: Measure of waiting time distribution (1.0 = perfectly fair)
• Efficiency Ratio: Ratio of useful work to total time

🤖 Gemini AI Integration

The simulator includes a built-in AI advisor powered by Google's Gemini 1.5 that can:

• Explain Algorithms: Detailed explanations of FCFS, SJF, Priority, and Round Robin
• Analyze Results: Interpret your simulation metrics and provide insights
• Give Recommendations: Suggest the best algorithm for your workload
• Answer Questions: General OS and scheduling questions

Using the Chatbot

1. Run a simulation first (provides context)
2. Go to "🤖 AI Advisor" tab
3. Type your question or use quick action buttons
4. Get AI-powered responses with clear formatting

📁 Project Structure

cpu-scheduling-simulator/
├── app.py                 # Main application entry point
├── requirements.txt       # Python dependencies
├── README.md             # This file
├── TODO.md               # Development checklist
├── assets/
│   └── style.css         # Custom CSS styling
├── core/
│   ├── __init__.py
│   ├── algorithms.py     # Scheduling algorithms (FCFS, SJF, etc.)
│   ├── gantt.py          # Gantt chart generation
│   └── metrics.py        # Performance metrics calculation
├── ui/
│   ├── __init__.py
│   ├── layout.py         # Page layout and styling
│   ├── inputs.py         # User input handling
│   ├── results.py        # Results display and export
│   └── chatbot.py        # Gemini AI integration
├── report/
│   ├── manual_computation.md
│   ├── os_analysis.md
│   └── screenshots/
└── utils/                # Utility functions

🎓 Educational Use

This simulator is perfect for:

• Students learning operating systems concepts
• Instructors teaching CPU scheduling
• Developers understanding algorithm behavior
• Researchers analyzing scheduling performance

Learning Path

1. Start with predefined scenarios to understand different workloads
2. Experiment with custom process configurations
3. Compare algorithms to see their strengths and weaknesses
4. Use the AI advisor to deepen understanding
5. Export results for assignments or reports

🔧 Configuration

API Key Setup

The Gemini API key is pre-configured in ui/chatbot.py:

GEMINI_API_KEY = "YOUR_API_KEY"

To use your own API key:

1. Get a key from Google AI Studio
2. Replace the key in ui/chatbot.py
3. Restart the application

Customization

• Edit assets/style.css to change colors and styling
• Modify ui/layout.py to adjust page structure
• Update core/algorithms.py to add new scheduling algorithms

🐛 Troubleshooting

Common Issues

Module Not Found Error

pip install -r requirements.txt

Streamlit Not Running

# Check if streamlit is installed
pip show streamlit

# Reinstall if needed
pip install --upgrade streamlit

Gemini API Errors

• Check internet connection
• Verify API key is valid
• Check API quota limits

Port Already in Use

streamlit run app.py --server.port 8502

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Development Setup

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add some AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• Streamlit for the amazing web app framework
• Google Gemini for AI capabilities
• Plotly for interactive visualizations
• FPDF for PDF export functionality

👨‍💻👩🏻‍💻 Developer/Contributors

• Arron Kian Parejas (App Developer)
• Graciella Mhervie Jimenez (Project Manager)
• Jenica Sarah Tongol (Documentation/QA)
• Jian kalel Marquez (Unit Testing/Documentation)

📧 Contact

For questions or suggestions, please open an issue on GitHub or contact the maintainer.

---

Happy Scheduling! 🖥️⚡

Built with ❤️ using Streamlit and Gemini 3.0