Distance Vector Algorithm Python

Distance Vector Algorithm

Idea: p2p network that has sets of nodes and undirected costs in form (node1,node2,cost) and every node has routing table that consists of destination node, cost and outgoing link.

Program (requirements):

- Read from a text file multiple entries of form (node1, node2, cost)

- Calculate distance-vector algorithm with periodic exchanges

- Update node tables for each iteration

A user should be able to:

o compute routing tables for any preset number of exchanges or until stability is achieved;

o preset any link to change cost or fail after any chosen exchange (you may assume for simplicity that neighbours notice cost changes or unreachable neighbours immediately);

o view the best route between any source and destination after any chosen iteration

o trace the routing tables of any set of nodes for any specified number of iterations in a way that can be easily viewed;

o engage, on request, a split-horizon capability to help combat slow convergence

o run the application under Windows 10 x64.

Once your simulator is working, document a walkthrough of two simple examples with appropriate input networks to illustrate how some exemplar routes and tables evolve under:

o normal convergence of the distance-vector algorithm;

o slow convergence both with and without the split horizon facility switched on.

Distance Vector Algorithm
Idea: p2p network that has sets of nodes and undirected costs in form (node1,node2,cost) and every node has routing table that consists of destination node, cost and outgoing link.
Program (requirements):
- Read from a text file multiple entries of form (node1, node2, cost)
- Calculate distance-vector algorithm with periodic exchanges
- Update node tables for each iteration


A user should be able to:
o compute routing tables for any preset number of exchanges or until stability is achieved;
o preset any link to change cost or fail after any chosen exchange (you may assume for simplicity that neighbours notice cost changes or unreachable neighbours immediately);
o view the best route between any source and destination after any chosen iteration
o trace the routing tables of any set of nodes for any specified number of iterations in a way that can be easily viewed;
o engage, on request, a split-horizon capability to help combat slow convergence
o run the application under Windows 10 x64.
Once your simulator is working, document a walkthrough of two simple examples with appropriate input networks to illustrate how some exemplar routes and tables evolve under:
o normal convergence of the distance-vector algorithm;
o slow convergence both with and without the split horizon facility switched on.

Java Python

Project ID: #18839346

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