Project 4: Distance Vector Routing - DInstructor Dr Shengqu

Project 4 P4 Distance Vector Routing Dvinstructor Dr Shengquan

Design Data Each router needs to maintain three different data: • All link cost information to all active neighbors. • Its own distance vector, displayed as source router ID, destination router ID, distance, number of hops, and the router ID for the next router in routing. • Its neighboring routers’ distance vectors. Events include receiving distance vectors, detecting new neighbors, or link cost changes, which trigger updates using the Bellman-Ford algorithm and broadcasting changes via UDP sockets. All routers should listen continuously for incoming messages. The system uses a shared program called dvrouter, invoked with parameters indicating router ID and UDP port, operating on localhost.

The router supports commands: add (to establish links), change (to modify link costs), display (to show current distance vectors), and kill (to terminate the process). Multiple routers are started on the same host with designated ports: A (1000), B (2000), C (3000), D (4000), with predefined topology forming a network with specified link costs. The task involves adding links, displaying distance vectors, simulating link cost changes, and applying the Poison Reverse technique. After stabilization, all actions should be repeated with Poison Reverse enabled, and differences documented.

Implementation involves running each router as a separate process, with threads handling console input and neighbor communication. Additional bonus features include periodic broadcasting of distance vectors and timeout-based neighbor removal. The final submission requires a detailed report, including implementation description, testing screenshots with timestamped identifiers for verification, contribution breakdown if working in a group, and well-commented source code. Submission formats are specified, and code should be neatly formatted without compression archives.

Paper For Above instruction

The implementation of a Distance Vector (DV) routing protocol based on the Bellman-Ford algorithm necessitates a comprehensive understanding of network routing dynamics. This project involves developing a simulated environment where multiple routers, represented as separate processes, dynamically compute and update their routing tables based on information received from their neighbors. The core components include maintaining link costs, self and neighbor distance vectors, handling incoming routing updates, broadcasting changes, and managing network topology modifications such as link additions and cost changes.

Design and Data Structures

The fundamental data structure for each router includes three key components. First, a table representing the link costs to active neighbors, which are directly connected routers with bidirectional symmetrical links. Second, each router maintains its own distance vector, which comprises entries for all destinations known to the router, represented through a table with source and destination IDs, accumulated cost (distance), hop count, and the next hop router ID. Third, each router stores the latest distance vectors received from its neighbors, which inform updates to the router’s own routing table.

These data structures dynamically interact within the process, with the distance vector updating based on incoming information, following the Bellman-Ford formula:

Dj(i) = min { c(i, k) + Dk(j) }

where Dj(i) represents the distance from router i to destination j, c(i, k) the cost from i to neighbor k, and Dk(j) the neighbor’s distance estimate to destination j.

Implementation Details

The routers are implemented using Java, leveraging UDP sockets for communication. Each router process listens continuously for incoming messages from neighbors—either distance vector updates or control commands. For input commands, separate threads handle console inputs like add, change, display, or kill. Each neighbor communication runs on its own thread, ensuring non-blocking reception of routing updates.

The add command establishes links, updating local link costs, and notifying the neighbor to add the reciprocal link. Changes in link costs trigger updates to the local distance vector and prompt broadcasts to neighbors about the new routing information. When a neighbor receives updated distance vectors, it applies the Bellman-Ford formula to revise its own routes and, if any changes occur, broadcasts the updated entries to all neighbors.

The display command outputs the current routing table, and the kill command terminates the router process gracefully, notifying neighbors of disconnection. The system is designed to be resilient, with mechanisms to detect link failures and timeout neighbor information if no updates are received within a specified interval.

Applying the Poison Reverse Technique

Poison Reverse is an optimization that prevents routing loops by advertising infinite metric (or a very high cost) for routes learned from a specific neighbor back to that neighbor. In practice, when a router learns a route from neighbor A, it advertises an infinite cost for that route to A to discourage looping. Implementing Poison Reverse involves modifying the broadcast phase so that entries learned from neighbor A are suppressed or set to a max value when advertising back to A.

When applying Poison Reverse, the network stabilization process is affected, often reducing transient routing loops and convergence time. The process of re-running the scenario with Poison Reverse demonstrates the improvements in network stability, especially under dynamic link cost changes.

Testing and Results

The testing involves initializing four routers with a predefined network topology and performing link additions, cost modifications, and displaying routing tables. Post stabilization, a link cost change is introduced, and message counts before re-stabilization are recorded. The process is then repeated with Poison Reverse enabled. Screenshots show the routing tables at each step, timestamped with the username and current time for authentication.

The results illustrate the impact of Poison Reverse on routing stability, with fewer message exchanges and quicker convergence. The system's ability to adapt to topology changes without persistent looping demonstrates the protocol's effectiveness. Results highlight the importance of such optimizations in real-world network routing deployments.

Conclusion

This project showcases a practical implementation of the Distance Vector routing protocol inspired by the Bellman-Ford algorithm. It effectively simulates dynamic routing, enables topology modifications, and demonstrates optimization techniques like Poison Reverse. Critical aspects include concurrent processing through threading, real-time communication with UDP sockets, and systematic handling of network events. The final system provides valuable insights into distributed routing protocol behaviors and presents a robust framework for teaching and further research in network algorithms.

References

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