This Python program is a simulation of the Chandy-Misra-Haas algorithm for distributed deadlock detection in the AND model. It serves as an educational tool to understand the algorithm's implementation and how it detects deadlocks in a distributed system.
Python 3.6 or higher
- Navigate to the directory containing the chandy_misra_haas.py script.
- Run the script using the command:
python chandy_misra_haas.pyorpython3 chandy_misra_haas.py - Observe the output to see if a deadlock has been detected.
You can modify the process_connections dictionary in the main() function of the chandy_misra_haas.py script to test different scenarios.
The keys of the dictionary represent process IDs, and the values are lists of dependent process IDs:
process_connections = {
0: [1, 2],
1: [2],
2: [0, 3],
3: [4],
4: [1]
}This implementation is a simplified simulation of a distributed system and is not suitable for use in real distributed systems. It is designed for educational purposes to help understand the Chandy-Misra-Haas algorithm for the AND model.
The Chandy-Misra-Haas algorithm is a distributed deadlock detection algorithm for the AND model. In the AND model, a process is considered deadlocked if it is waiting for all of its requested resources to be granted. In this algorithm, each process maintains a local wait-for graph and sends probe messages to its dependent processes. The algorithm detects a deadlock when a process receives a probe message with its own identifier.
The provided Python implementation defines a Process class that represents a process in a distributed system. Each process has a unique id, a list of dependent_processes, a local wait_for_graph, and a message_queue. The run method of the Process class listens for incoming messages and calls the send_probe method to send probe messages to dependent processes.
The setup_processes function initializes the processes with their dependencies, and the main function sets up the threads for each process and initiates the deadlock detection.