Performance vs. Cost Analysis of WDM Networks with Dynamic Traffic Grooming Isabella Cerutti, Andrea Fumagalli, Sonal Sheth Optical Networks Advanced Research (OpNeAR) Lab The University of Texas at Dallas Traffic Grooming in WDM Networks Performance vs. Cost Optimization Problems Offline Network Design Given: N = number of nodes in the network L = number of bidirectional lines in the network Hp = average hop length of a lightpath find the optimal ratio of: W = number of wavelengths on each network line Ti /Ri = number of transmitters/receivers at node i FG: Ti = Ri = W · # of lines from/to node i SH: Ti = Ri ≈ MH: Ti = Ri ≈ , where H = average hop length of a lightpath fixed a priori for different MH designs (1 ≤ H ≤ Hp ) First Generation (FG): connections are multiplexed in any node (lightpaths are only between adjacent nodes) Single-Hop (SH): connections are multiplexed only at the terminal nodes of the end-to-end lightpaths Multi-Hop (MH): connections can be multiplexed at selected intermediate nodes Given a fixed budget to design a WDM network with grooming capabilities, which architecture provides the best performance? Offline problem: Minimize the network resources to support a given traffic load Trade-off between reduced number of wavelengths (FG) and reduced number of transmitters/receivers (SH) Online problem: Minimize the blocking probability of connection requests, for a given WDM network design d s Lightpaths: SH FG MH Online Connection Provisioning Online Algorithm Based on auxiliary graph G(V,E): Add an edge between node pairs (i, j) to G(V,E) if: A lightpath already exists and has bandwidth to accommodate the new connection request (type 1 edge) A lightpath can be created, i.e., it is available a wavelength on any line along the shortest path between (i, j), a transmitter at node i and a receiver at node j (type 2 edge) Run shortest path on G(V,E) to route the connections, using resources cost as link weights Solve jointly the following sub-problems: Virtual topology re-design Lightpath routing on the physical topology (shortest path algorithm) Wavelength assignment to the lightpath(s) End-to-end connection routing on the virtual topology (wavelength ID, used capacity) Example Physical topology Auxiliary graph G(V,E) W = 3, Ti = Ri = 6, connection bandwidth = 0.2 Ring Network Mesh Network Conclusion For a fixed budget, MH outperforms SH and FG MH advantageous in poorly connected topology (ring) and with high wavelength grooming capabilities For a fixed budget, FG may outperform SH, depending on the line-to-node cost ratio and overall budget $ Connection blocking probability vs. normalized arrival rate $ (i.e., arrival rate/design cost) N = L = 14, Hp = 3.77 , grooming factor: g = 10, line-to-node cost ratio: γ = 0.5 Connection blocking prob. vs. $, for γ = 0.1 and 0.9 NSFNET topology: N = 14, L = 21, g = 10, Hp = 2.14