CPU Scheduling Analysis and System Error Detection

Project Overview

This project analyzes CPU scheduling algorithms and system errors by gathering real-time system metrics, training machine learning models, and visualizing performance through an interactive dashboard.

Objectives

1. System Monitoring: Collect CPU, memory, and disk usage metrics using psutil.
2. Scheduling Analysis: Evaluate FCFS, SJF, Round Robin, and Priority Scheduling.
3. Error Detection: Log system errors and deadlocks for better performance insights.
4. Predictive Modeling: Train ML models to forecast CPU utilization and response time.
5. Visualization: Display insights in an interactive dashboard using Dash and Plotly.

Project Structure

1. Collecting Data Directory main.py (Data Collection & Preprocessing)

• Uses psutil to collect system metrics.
• Stores data in MySQL (cpu_scheduling database).
• Implements CPU scheduling algorithms (FCFS, SJF, RR, Priority Scheduling).
• Logs system events and errors.

2. Training and Evaluating the Data Directory main.py (Machine Learning Model Training)

• Fetches data from MySQL (performance table).
• Prepares dataset with CPU utilization, turnaround time, waiting time, response time.
• Trains Random Forest & Gradient Boosting Regressor models.
• Stores model evaluation metrics (MSE, R2, MAE) in MySQL (model_evaluation table).

3. Visualizing the data Directory main.py (Data Visualization Dashboard)

• Fetches performance metrics from MySQL.
• Uses Dash & Plotly to create an interactive CPU Scheduling Performance Dashboard.
• Includes:
  • CPU Utilization Over Time (Line Chart)
  • Comparison of Scheduling Metrics (Bar Chart)

🛠 Setup & Installation

Required Installations

Run the following command to install all dependencies:

pip install psutil mysql-connector-python pandas numpy scikit-learn dash plotly matplotlib seaborn

Breakdown by File

1. Collecting Data Directory main.py (System Monitoring & Data Collection)

   pip install psutil mysql-connector-python

2. Training and Evaluating the Data Directory main.py (Machine Learning Model Training)

   pip install pandas numpy scikit-learn mysql-connector-python

3. Visualizing the data Directory main.py (Dashboard & Visualization)

   pip install dash plotly pandas mysql-connector-python

Prerequisites

• Python 3.8+
• MySQL Server
• Required Python libraries:

  pip install psutil mysql-connector-python pandas numpy scikit-learn dash plotly

Database Setup

1. Create the MySQL database:

   CREATE DATABASE cpu_scheduling;

2. Run main.py, which creates tables and collects system metrics.

How to Run the Project

1. Run Data Collection (main.py)

python main.py

• Gathers system metrics and stores them in MySQL.
• Runs CPU scheduling algorithms and logs performance.

2. Train Machine Learning Models (main.py)

python main.py

• Fetches data from MySQL and trains models.
• Stores model evaluation metrics.

3. Start Dashboard (main.py)

python main.py

• Launches the interactive dashboard on http://127.0.0.1:8080/.
• Select a scheduling algorithm to view insights.

Note

-- **Before running each file check the file locations properly

Expected Deliverables

✔ Real-time system monitoring and error logging.
✔ Performance evaluation of different CPU scheduling algorithms.
✔ Machine learning models for predicting CPU utilization trends.
✔ Interactive dashboard for visualizing scheduling performance.

Future Enhancements

• Integrate real-time alerts for system errors & deadlocks.
• Improve predictive models with deep learning.
• Enhance visualization with more analytics & user controls.

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Contributors

• Contributions are welcome! Feel free to open issues or submit pull requests to enhance the project.