Implementation of Distributed Algorithms

Implementation of Distributed Algorithms

Distributed algorithms are designed to solve problems in distributed systems, where multiple nodes collaborate to achieve a common goal. One fundamental problem in distributed computing is distributed mutual exclusion, which ensures that only one process can access a shared resource at a time across multiple nodes.

The implementation of distributed mutual exclusion typically involves the use of message passing and coordination protocols to ensure correctness and fairness.

A classic algorithm for distributed mutual exclusion is the Ricart-Agrawala algorithm, which uses a request-acknowledge mechanism to grant access to critical sections.

Distributed Mutual Exclusion Algorithm: Ricart-Agrawala

The following is a simplified implementation of the Ricart-Agrawala algorithm in Python:

        from threading import Thread, Lock
        import time

        num_processes = 3
        process_id = 0
        requests = [0] * num_processes
        replies = [0] * num_processes
        critical_section_access = False
        waiting_for_reply = False
        lock = Lock()

        def request_cs():
            global requests, replies, waiting_for_reply
            with lock:
                requests[process_id] = time.time()
                waiting_for_reply = True
                for i in range(num_processes):
                    if i != process_id:
                        send_request(process_id, i)

        def send_request(sender_id, receiver_id):
            # Simulate message passing
            time.sleep(0.5)
            receive_request(sender_id, receiver_id)

        def receive_request(sender_id, receiver_id):
            global requests, replies, waiting_for_reply, critical_section_access
            with lock:
                if critical_section_access or (waiting_for_reply and requests[sender_id] < requests[receiver_id]):
                    send_reply(sender_id, receiver_id)
                else:
                    replies[receiver_id] += 1

        def send_reply(sender_id, receiver_id):
            # Simulate message passing
            time.sleep(0.5)
            receive_reply(sender_id, receiver_id)

        def receive_reply(sender_id, receiver_id):
            global replies, waiting_for_reply
            with lock:
                replies[receiver_id] += 1
                if all(reply >= num_processes - 1 for reply in replies):
                    enter_critical_section()

        def enter_critical_section():
            global critical_section_access, waiting_for_reply
            print(f"Process {process_id} entering critical section.")
            time.sleep(1)
            print(f"Process {process_id} exiting critical section.")
            critical_section_access = False
            waiting_for_reply = False

        if __name__ == "__main__":
            threads = []
            for i in range(num_processes):
                threads.append(Thread(target=request_cs))
            for thread in threads:
                thread.start()
            for thread in threads:
                thread.join()
    

This code demonstrates a simple simulation of the Ricart-Agrawala algorithm in a distributed environment. Each process requests access to the critical section and waits for replies from other processes before entering the critical section.