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A Waveband Switching Architecture and Algorithm for Dynamic Traffic IEEE Communications Letters, Vol.7, No.8, August 2003 Xiaojun Cao, Vishal Anand, Chunming.

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Presentation on theme: "A Waveband Switching Architecture and Algorithm for Dynamic Traffic IEEE Communications Letters, Vol.7, No.8, August 2003 Xiaojun Cao, Vishal Anand, Chunming."— Presentation transcript:

1 A Waveband Switching Architecture and Algorithm for Dynamic Traffic IEEE Communications Letters, Vol.7, No.8, August 2003 Xiaojun Cao, Vishal Anand, Chunming Qiao Presented by Shaun Chang

2 A Study of Waveband Switching With Multilayer Multigranular Optical Cross- Connects IEEE Journal on Selected Areas in communications, Vol.21, No.7, Sep Xiaojun Cao, Vishal Anand, Yizhi Xiong, Chunming Qiao Presented by Shaun Chang

3 3 Outline Introduction Static Traffic Dynamic Traffic

4 4 Introduction DWDM technology brought about a tremendous increase in the size of OXCs. It incurred the cost and difficulty to control such large OXCs. One of the major factors contributing the cost and complexity of OXC is its port count. Waveband switching groups several wavelengths together as a band, and to switch the band using a single port.

5 5 Example Ten fibers, 100 wavelengths/fiber, and one wavelength needs to be dropped and one to be added at a node. Ordinary-OXC: 999 ports for bypass, 2 for drop and add. Total:1001 ports MG-OXC: Each fiber is grouped into 20 bands, each band has 5 wavelengths. 9 ports for bypass fiber, 2 ports for FTB, BTF, 19 ports for bypass waveband, 2 ports for BTW, WTB, 4 ports for bypass wavelength, 2 ports for add and drop. Total: 38 ports.

6 6 Introduction

7 7 Outline Introduction Static Traffic Architecture Waveband Switching ILP Formulation Conclusion Dynamic Traffic

8 8 Architecture

9 9 Traditional RWA Objective Minimize the total number of wavelength- hops (WHs) Maximum number of wavelengths required to satisfy a given set of lightpaths requests.

10 10 Outline Introduction Static Traffic Architecture ILP Formulation BPHT Heuristic Conclusion Dynamic Traffic

11 11 Notations

12 12 Notations (cont’d)

13 13 Decision Variables

14 14 Decision Variables (cont’d)

15 15 Waveband at node n

16 16 Objective function

17 17 RWA Constraints

18 18 Waveband Switching Constraints (cont’d)

19 19 Waveband Switching Constraints (cont’d)

20 20 Port number Constraints (cont’d)

21 21 Outline Introduction Static Traffic Architecture ILP Formulation BPHT Heuristic Conclusion Dynamic Traffic

22 22 Balanced Path Routing with Heavy- traffic First Waveband Assignment Grouping the lightpaths with the same SD pair. Grouping the lightpaths from the same source. Grouping the lightpaths with same destination. Grouping the lightpaths with common intermediate links.

23 23 BPHT Stage 1: Balanced Path Routing Achieve load balanced routing Stage 2: Wavelength Assignment Assign the wavelengths to those bypass lightpaths first. Give preference to the lightpaths that overlap with many other shorter lightpaths to max the advantage of wavebanding. Stage 3: Waveband Switching Switch as many fibers using FXCs as possible Then as many wavebands using BXCs as possible.

24 24 Performance Metrices

25 25 Numerical Results (six nodes)

26 26 Numerical Results

27 27

28 28 Outline Introduction Static Traffic Architecture ILP Formulation BPHT Heuristic Conclusion Dynamic Traffic

29 29 Conclusions We have developed a corresponding ILP formulation, and an efficient near-optimal heuristic algorithm. With appropriate waveband granularity, using MG-OXCs can save up to 50% ports in single-fiber networks and up to 70% ports in multifiber networks, when compared with using ordinary-OXCs.

30 30 Outline Introduction Static Traffic Dynamic Traffic Architecture MILB Algorithm Numerical Results Conclusion

31 31 Objective Reduce the number of used ports Bypass traffic accounts for 60%~80% of the total traffic in the backbone. Reduce the cost of MG-OXC Reduce the blocking probability of dynamic connection request

32 32 Architecture

33 33 Ports Count Number of Ports used: The port ratio of MG-OXC to ordinary-OXC

34 34 Single fiber system For single fiber systems, α must be 1 to allow any fiber to be demultiplexed to bands. We can limit the value of β to less than 1 by allowing only a limited number of bands to be demultiplexed to wavelengths. We study the effects of varying β. Assume no wavelength or waveband conversion

35 35 Outline Introduction Static Traffic Dynamic Traffic Architecture MILB Algorithm Numerical Results Conclusion

36 36 Maximum Interference Length in Band (MILB) Algorithm

37 37 Layered band-graph approach

38 38 Outline Introduction Static Traffic Dynamic Traffic Architecture MILB Algorithm Numerical Results Conclusion

39 39 New connection request has a random source and destination Smaller β results in more savings in port count, but might cause higher blocking probability.

40 40

41 41 Numerical Result Random-Fit The RWA doesn’t consider how to group the lightpaths into bands appropriately. First-Fit Assign wavelengths to lightpaths sequentially which helps in wavebanding and thus reducing the number of used ports. When β<0.65, MILB is better than First-Fit, and much better than Random-Fit.

42 42 Outline Introduction Static Traffic Dynamic Traffic Architecture MILB Algorithm Numerical Results Conclusion

43 43 Conclusion We proposed an efficient heuristic algorithm, MILB to do RWA for WBS in conjunction with a reconfigurable MG-OXC architecture for dynamic traffic. Our heuristic is especially useful for reducing network operating costs by minimizing the number of used ports in a WDM network.


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