Interactive Deadlock Analyzer

An educational GUI desktop application that simulates process-resource interactions, detects deadlocks, and predicts unsafe states using Banker's Algorithm and Resource Allocation Graphs (RAG).

Features

• Banker's Algorithm Integration: Dynamically calculates Safe/Unsafe states using the Max, Allocation, Need, and Available matrices.
• Interactive Simulation: Step forward and backward through system states to analyze how decisions impact system safety.
• Dynamic Resource Management: Add new processes and resources on the fly.
• Resource Allocation Graph (RAG) Visualization: Real-time generation of bipartite directed graphs using NetworkX and Matplotlib to visualize allocations and requests.
• Deadlock Detection: Advanced graph reduction algorithms detect cycles and definitive deadlocks in multi-instance environments.
• Override Functionality: Bypass Banker's Algorithm to force resource allocations, deliberately creating unsafe states and circular waits to observe deadlock occurrences.

Tech Stack

• Language: Python 3
• GUI Framework: PyQt5
• Graph Visualization: NetworkX & Matplotlib (embedded via FigureCanvasQTAgg)
• Architecture: Model-View-Controller (MVC)

Installation

1. Ensure you have Python 3 installed.
2. Install the required dependencies:

   pip install PyQt5 numpy networkx matplotlib

Usage

1. Clone the repository and navigate to the project directory.
2. Run the main application:

   python main.py

Interface Overview

• Left Panel (Controls): Create resources, add processes (with specific max claims), and simulate requests or releases. You can also bypass the Banker's Algorithm here by checking the force allocation box.
• Right Panel (Visualization): Displays the dynamic system status (Safe, Unsafe, Deadlocked, etc.) and visualizes the Resource Allocation Graph. Processes are light blue circles; Resources are light green squares. Solid green arrows denote allocations, and dashed red arrows denote requests.
• Bottom Panel (Matrices): View real-time updates to the Max Claim, Allocation, Need, and Available matrices.

Project Structure

• main.py: The central controller that wires the UI and Backend Engine.
• ui/main_window.py: The programmatic PyQt5 dashboard view.
• views/canvas.py: The customized Matplotlib Canvas for NetworkX bipartite graph rendering.
• engine/simulator.py: State manager handling step histories and Banker's logic integration.
• engine/banker.py: Implementation of Banker's Algorithm for safety sequences.
• engine/graph_utils.py: RAG construction, cycle detection, and graph-reduction-based deadlock detection.