Part III

1. Part III Wide-Area (Wavelength-Routed) Optical Networks – Virtual Topology Design Wavelength Conversion Control and Management BM-UC Davis

2. Lightpaths and Wavelength Routing • Lightpath • Virtual topology • Wavelength-continuity constraint • Wavelength conversion • Packet routing BM-UC Davis

3. Illustrative example NY WA MI NJ PA UT CA1 CO IL NE MD CA2 GA TX BM-UC Davis

4. Solution 1a: Infocom’94 and ToN-Oct96 • More than one laser filter pair at any node can tune to the same wavelength BM-UC Davis

5. Solution 1b: Infocom’94 and ToN-Oct96 • All laser filter pairs at any node must be tuned to different wavelengths BM-UC Davis

6. Virtual Topology BM-UC Davis

7. Wavelength Routing Switch (WRS)–Details of the UT Node BM-UC Davis

8. New optimality criterion (c) Minimize average hop distance Optimization Problem Formulation • On virtual topology traffic variables sdij • On virtual topology connection matrix Vij • On physical route variables pijmn • On coloring of lightpaths cijk non-linear! • Objective: Optimality criterion (a) Delay minimization: (b) Maximizing offered load (equivalent to minimizing maximum flow in a link): BM-UC Davis

9. Solution Approach to Virtual Topology WDM WAN Design 1. Choice of optimal virtual topology • Simulated annealing; optimization based on maximizing throughput, minimizing delay, maximizing single-hop traffic, etc. 2.Routing of lightpaths over the physical topology • Alternate-path routing, multicommodity flow formulation, randomized routing 3. Wavelength assignment: Coloring of lightpaths to avoid wavelength clashes • Graph-coloring algorithms, layered graph models 4. (Optimal) routing of packets over the virtual topology • Shortest-path routing, flow-deviation algorithm, etc. 5. Iterate • Check for convergence and go back to Step 1, if necessary. BM-UC Davis

10. Details of Virtual Topology Design • Simulated Annealing • Start with random virtual topology • Perform node exchange operations on two random nodes • Route packet traffic (optimally) using flow deviation • Calculate maximum trafficscaleup for current configuration • If maximum scaleup is higher then previous maximum, then accept current configuration; else accept current configuration with certain decreasing probability • Repeat until problem solution stabilizes (frozen). • Flow Deviation • Perform shortest-path routing of the traffic • Select path with large traffic congestion • Route a fraction of this traffic to less-congested links • Repeat above two steps iteratively, until solution is acceptable BM-UC Davis

11. NSFNET Traffic Matrix (11:45 PM to midnight, ET, Jan. 12, 1992) BM-UC Davis

12. The WDM Advantage BM-UC Davis

13. Delay Components in a WDM Solution BM-UC Davis

14. WDM (with P transmitters/receivers per node) • WDM Advantage IncreasingPdecreasingHv Scaling of Bandwidth – The WDM Advantage C = link speed (Mbps) Hp= avg. hop distance (physical) N = number of nodes • No WDM (Physical Topology) BM-UC Davis

15. Problems/Limitations of Solution 1 • Nonlinear objective functions. • Nonlinear constraints – on wavelength continuity. • Resorted to heuristics • Optimal virtual topology design (Simulated Annealing) • Optimal packet routing on V.T. (Flow Deviation Algorithm) • No routing and wavelength assignment(Shortest-path lightpath routing; no constraints on wavelengths). BM-UC Davis

16. Highlights/Contributions of Solution 2 • Complete Virtual Topology Design • Linear formulation  Optimal solution • Objective: Minimize average hop distance • Assume: Wavelength conversion(Sparse conversion provides almost full conversion benefits). • Resource Budgeting Tradeoffs • Important/Expensive Resources: Transceivers and wavelengths • Don’t under-utilize either of them! • Hardware cost model. • Optimal Reconfiguration Algorithm • Minimize reconfiguration time. BM-UC Davis

17. Optional Constraints / Simplifying Assumptions • Need scalability. • Physical topology is a subset of the virtual topology. • Bounded lightpath length • Prevent long convoluted lightpaths from occuring. • Prune the search space • Consider K shortest paths (bounded K). BM-UC Davis

18. (b) Five-wavelength solution Two Solutions from the LP (a) Two-wavelength solution BM-UC Davis

19. Hop Distance, Transceiver + Wavelength Utilization BM-UC Davis

20. Average Hop Distance BM-UC Davis

21. Transceiver Utilization BM-UC Davis

22. Wavelength Utilization BM-UC Davis

23. Heuristic Solutions BM-UC Davis

24. WDM Network Cost Model BM-UC Davis

25. Reconfiguration Algorithm • Generate linear formulations F(1) and F(2) corresponding to traffic matrices sd1 and sd2. • Derive solutions and S(1) and S(2), corresponding to F(1) and F(2) • Modify F(2) to F’(2) by adding the new constraint: • New objective function for F’(2) :or • Although mod is nonlinear, above reconfiguration formulation is linear since the variables p’s and V’s are binary. BM-UC Davis

26. Reconfiguration Statistics BM-UC Davis

27. Summary of Virtual Topology Design Principles • Use WDM to scale up an existing fiber-based WAN(Network’s information carrying capacity increased manifold) • Employ packet-switched virtual topology… imbedded on a physical topology… as if we have a virtual Internet(which is reconfigurable under user control)… need optimum graph-imbedding algorithms • Reuse electronic switch of existing WAN… as part of the WRS in the scaled-up WAN BM-UC Davis