Deadlock Simulator (Interactive Resource Allocation Graph Visualizer & Analyzer)

An interactive desktop application to simulate and visualize resource allocation in operating systems. Users can create processes and resources, allocate/release resources, visualize the Resource Allocation Graph (RAG), detect deadlocks, and analyze system states.

---

Features

• Interactive Resource Management

  • Allocate resources to processes.
  • Release resources and automatically assign to waiting processes.
  • Log all actions in a status window.

• Deadlock Detection

  • Detects deadlocks using cycle detection in the resource allocation graph.
  • Highlights deadlocked processes and resources in red.
  • Provides clear textual messages for detected deadlocks.

• Graph Visualization

  • Processes (light blue), Resources (light green)
  • Deadlocked nodes highlighted in red
  • Allocation edges: gray, Request edges: red dashed
  • Dynamic updates based on user actions.

• Clear All

  • Resets all processes, resources, and requests.

---

Learning Objectives Covered

1. Model OS Resource Allocation Concepts:

   • Uses networkx directed graphs to represent processes, resources, allocation, and request edges.

2. Implement Deadlock Detection & Safety Check Algorithms:

   • Detects cycles in the resource allocation graph to identify deadlocks.
   • Reports cycles clearly in textual logs and highlights affected nodes visually.

3. Build Interactive UI:

   • Tkinter-based GUI translates user inputs (allocate/release) into graph transformations and triggers deadlock detection automatically.

4. Analyze & Report Results:

   • Status window logs all operations.
   • Visualization clearly represents system state and deadlocks.

---

File Structure

.
├── src
│   ├── gui.py               # Main GUI application
│   ├── allocation.py        # Backend resource allocation and deadlock detection
│   └── visualization.py     # Graph visualization module
├── output
│   ├── scenario1.png        # Example output screenshots
│   ├── scenario2.png
│   └── scenario3.png      
└── README.md

---

Installation

1. Clone the repository:

git clone https://github.com/zuni-developer/Deadlock-Simulator
cd Interactive-resource-allocation-graph-simulator

2. Install dependencies:

pip install networkx matplotlib

---

Usage

Run the main application:

python src/gui.py

GUI Components

• Process Entry: Enter the process name.
• Resource Entry: Enter the resource name.
• Buttons:
  • Allocate: Allocate the resource to the process (or request if already allocated).
  • Release: Release the resource from the process.
  • Check Deadlock: Detect deadlocks and display warnings.
  • Clear All: Reset the simulation.
• Status Window: Logs messages for all actions.
• Visualization Canvas: Dynamically shows the resource allocation graph.

---

Example Scenarios

Scenario 1: Simple Allocation

• Process P1 allocated to resource R1.
• Resource allocation edges shown in gray.

Scenario 2: Deadlock Detection

• Circular wait detected: P1 -> R1 -> P2 -> R2 -> P1.
• Deadlocked nodes highlighted in red.
• Request edges shown as red dashed lines.

Scenario 3: Resource Release

• Released R1 from P1.
• Waiting processes automatically allocated resources.
• Graph updated dynamically.

---

Implementation Details

• Graph Representation:

  • Nodes: Processes and resources
  • Edges:
    • Allocation (Resource → Process)
    • Request (Process → Resource)

• Deadlock Detection:

  • Uses networkx.simple_cycles to detect cycles with both allocation and request edges.

• Visualization:

  • matplotlib integrated with Tkinter canvas for dynamic updates.

---

Future Improvements

• Implement safety algorithm to check safe states.
• Suggest recovery actions for deadlocks (terminate process, preempt resource).
• Add multiple resource types and batch allocations.
• Export/Import simulation states for reproducibility.

---

📌 About This Fork

This project is forked from the original repository:
Interactive Resource Allocation Graph Simulator
by Ch Shiva Kalyan, Abhiram, and Mohd Subhan.

Modifications in This Fork

• Rewritten and expanded README documentation
• Improved project structure and file organization
• Enhanced feature descriptions and learning objectives
• Added example scenarios and future improvement roadmap

---