1. Optical Networks BM-UC Davis122 Part III Wide-Area (Wavelength-Routed) Optical Networks – 1.Virtual Topology Design 2.Wavelength Conversion 3.Control and Management

2. Optical Networks BM-UC Davis123 Lightpaths and Wavelength Routing Lightpath Virtual topology Wavelength-continuity constraint Wavelength conversion Packet routing

3. Optical Networks BM-UC Davis124 Illustrative example WA CA1 CA2 UT CO TX NE IL MI NY NJ PA MD GA

4. Optical Networks BM-UC Davis125 Solution 1a: Infocom’94 and ToN-Oct96 More than one laser filter pair at any node can tune to the same wavelength

5. Optical Networks BM-UC Davis126 Solution 1b: Infocom’94 and ToN-Oct96 All laser filter pairs at any node must be tuned to different wavelengths

6. Optical Networks BM-UC Davis127 Virtual Topology

7. Optical Networks BM-UC Davis128 Wavelength Routing Switch (WRS)–Details of the UT Node

8. Optical Networks BM-UC Davis129 Optimization Problem Formulation On virtual topology connection matrix V ij On physical route variables p ij mn On virtual topology traffic variables sd ij On coloring of lightpaths c ij k Objective: Optimality criterion (a) Delay minimization: (b) Maximizing offered load (equivalent to minimizing maximum flow in a link): New optimality criterion (c) Minimize average hop distance

9. Optical Networks BM-UC Davis130 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.

10. Optical Networks BM-UC Davis131 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 traffic scaleup 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

11. Optical Networks BM-UC Davis132 NSFNET Traffic Matrix (11:45 PM to midnight, ET, Jan. 12, 1992)

12. Optical Networks BM-UC Davis133 The WDM Advantage Transceivers /node Scaleup 4106 5135 6163

13. Optical Networks BM-UC Davis134 Delay Components in a WDM Solution

14. Optical Networks BM-UC Davis135 Scaling of Bandwidth – The WDM Advantage No WDM (Physical Topology) WDM (with P transmitters/receivers per node) WDM Advantage Increasing P  decreasing H v C = link speed (Mbps) H p = avg. hop distance (physical) N = number of nodes

15. Optical Networks BM-UC Davis136 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).

16. Optical Networks BM-UC Davis137 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.

17. Optical Networks BM-UC Davis138 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 ).

18. Optical Networks BM-UC Davis139 Two Solutions from the LP (a) Two-wavelength solution (b) Five-wavelength solution

19. Optical Networks BM-UC Davis140 Hop Distance, Transceiver + Wavelength Utilization

20. Optical Networks BM-UC Davis141 Average Hop Distance

21. Optical Networks BM-UC Davis142 Transceiver Utilization

22. Optical Networks BM-UC Davis143 Wavelength Utilization

23. Optical Networks BM-UC Davis144 Heuristic Solutions

24. Optical Networks BM-UC Davis145 WDM Network Cost Model

25. Optical Networks BM-UC Davis146 Reconfiguration Algorithm Generate linear formulations F(1) and F(2) corresponding to traffic matrices  sd 1 and  sd 2. 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.

26. Optical Networks BM-UC Davis147 Reconfiguration Statistics

27. Optical Networks BM-UC Davis148 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