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PROFITABLE CONNECTION ASSIGNMENT IN ALL OPTICAL WDM NETWORKS

PROFITABLE CONNECTION ASSIGNMENT IN ALL OPTICAL WDM NETWORKS. VISHAL ANAND LANDER (Lab. for Advanced Network Design, Evaluation and Research) In collaboration with: Tushar Katarki and Chunming Qiao CSE Dept., SUNY at Buffalo. Outline. Introduction Related work

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PROFITABLE CONNECTION ASSIGNMENT IN ALL OPTICAL WDM NETWORKS

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  1. PROFITABLE CONNECTION ASSIGNMENT IN ALL OPTICAL WDM NETWORKS VISHAL ANAND LANDER (Lab. for Advanced Network Design, Evaluation and Research) In collaboration with: Tushar Katarki and Chunming Qiao CSE Dept., SUNY at Buffalo

  2. Outline • Introduction • Related work • Maximum Profitability Problem • Concluding remarks • Questions and discussion Vishal Anand

  3. Introduction • Optical WDM networks - future backbone for wide area networks. • Physical Topology - Optical wavelength routers connected by fiber links. • Lightpath or connection - Path between two end nodes and a wavelength on that path. • No wavelength conversion - Any lightpath uses the same wavelength on all the links its path spans. Vishal Anand

  4. The RWA(Routing & Wavelength Assignment) Problem • Given : • a network topology • a set of traffic demands (or connection requests). • Determine the routes and wavelengths to use so as to satisfy the demands. • The RWA problem is usually solved to optimize some specified objective(s). Vishal Anand

  5. Previous and related work • Example objectives • Minimize network-wide packet delay (e.g. number of hops). • Maximize throughput (e.g. number of lightpaths). • Maximize allowable capacity upgrade or scalability (for future traffic demands). • Minimizing cost (network resources used) can also be an important objective. • For a bandwidth broker (or carrier) maximizing profits is most important. Vishal Anand

  6. The Maximum Profitability Problem • Given: • a set of connection requests, N. • a network topology. • earnings (revenue) Ei associated with each connection request, i. • cost of using any wavelength on a link l, Cl. • Solve the RWA problem to maximize the profit, P =Total Earnings - Total Costs. Vishal Anand

  7. Maximizing profit problem is a more general formulation. If Ei=E, for each connection/lightpath i (i.e., all connections have equal earnings) OR if n=N (i.e., all the connection requests have to be satisfied) then the problem is same as the minimizing cost problem. If Ei=E and if all connections/lightpaths have equal costs. Then the problem is the same as maximizing throughput problem. Hence a more direct study of the maximizing profit problem is necessary. Vishal Anand

  8. Network Model • Network topology considered: 16 node NSFNET. • Cost of using each wavelength on a link , is the same, but varies from link to link. • No wavelength conversion capabilities at any of the nodes. • Number of wavelengths on each link in the network is the same. Vishal Anand

  9. Heuristic based approach • The RWA problem is known to be NP Hard and hence computationally intractable. • Maximizing profit heuristic: MaxPro • Find a cheapest path for each connection request and compute the profit. • Sort the requests in the order of decreasing profit and store in a list. • Satisfy connection requests in decreasing order of profit (a greedy approach). • If a connection request is satisfied. • delete that connection from the sorted list. Vishal Anand

  10. If the cheapest path for a connection request is not available. • Re-compute a new cheapest path for only that connection request. • Compute the new profit for this connection request. • Insert this connection into the sorted list depending on the profit. • Repeat till no other connection request can be satisfied. Vishal Anand

  11. Results and Comparison • 1) Results obtained from MaxPro compared with: • a minimizing cost heuristic • a random assignment heuristic • 2) Results of Maxpro compared with the optimal results from integer linear program. Vishal Anand

  12. Comparison of the heuristics • MaxPro performs the best. • Better than a minimizing cost heuristic. Vishal Anand

  13. Integer Linear Model • Definitions: • W :Number of wavelengths on each link. • n, L :Number of nodes, links in the network. • :Earnings obtained by satisfying a connection request between nodes  and . • : Total number of alternate routes/paths available to reach node  from node . • : Cost of reaching node  from node  on route r. • : = 1, if link j is used by the route r between nodes  and , 0 otherwise. • : Number of connection requests between nodes  and . • : = 1, indicates that the connection between node  and  is routed on route r using wavelength k. Vishal Anand

  14. Objective function • Subject to: • The total number of lightpaths established between a node pair should not exceed the number of requests between that node pair. Vishal Anand

  15. And • The total number of lightpaths established on any link should not exceed the number of wavelength on that link. • A wavelength on a link can support at most one lightpath. • The Integrality constraint. Vishal Anand

  16. Comparison of Maxpro with ILP • MaxPro obtains on the average 90% of the results got from the ILP(optimal profit). Vishal Anand

  17. Summary and Future work • Formulated a RWA problem with the objective of maximizing the profit. • Proposed a maximizing profit heuristic. • Compared results of a profit maximizing heuristic with a minimizing cost heuristic and ILP. • Future work • Study the maximizing profit problem for the On-line traffic model. • Extend to cases where protection and restoration is required for the traffic. Vishal Anand

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