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Electrical and Computer Engineering University of Alberta 2nd Floor, 9107 – 116 Street

Factors Affecting the Efficiency of Demand-wise Shared Protection Brian Forst, Wayne D. Grover Contact: bforst@trlabs.ca, grover@trlabs.ca. Electrical and Computer Engineering University of Alberta 2nd Floor, 9107 – 116 Street Edmonton, Alberta, Canada T6G 2V4. Network Systems Group TRLabs

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Electrical and Computer Engineering University of Alberta 2nd Floor, 9107 – 116 Street

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  1. Factors Affecting the Efficiency of Demand-wise Shared ProtectionBrian Forst, Wayne D. GroverContact: bforst@trlabs.ca, grover@trlabs.ca Electrical and Computer Engineering University of Alberta 2nd Floor, 9107 – 116 Street Edmonton, Alberta, Canada T6G 2V4 Network Systems Group TRLabs 7th Floor, 9107 – 116 Street Edmonton, Alberta, Canada T6G 2V4

  2. Overview • Background • 1+1 APS • DSP • Methodology & Network Families • Design Results • Practical Methods to Increase Efficiency • Conclusion

  3. Overview • Background • 1+1 APS • DSP • Methodology & Network Families • Design Results • Practical Methods to Increase Efficiency • Conclusion

  4. 1+1 Automatic Protection Switching (APS) • Oldest and simplest protection architecture • Minimum 100% redundancy • Fastest protection X A B Working path

  5. Why Demand-wise Shared Protection (DSP)? • Renewed interest in 1:N DP APS as a network protection architecture • Promising aspects include: • Fast failure-response times • Full pre-failure cross-connection of backup paths • No fault localization required • Conceptually simple to understand • Technically possible to operate in current networks • Possibility of low redundancy/high efficiency protection • Motivation for this study: • In recent studies DSP is not exhibiting the low redundancy we expected it could achieve

  6. A B DSP Working paths

  7. A B DSP X

  8. A B DSP X

  9. Overview • Background • 1+1 APS • DSP • Methodology & Network Families • Design Results • Practical Methods to Increase Efficiency • Conclusion

  10. Methodology • Obtain DSP and APS design results • Hop-based costs • Distance-based costs • Analyze route-cost statistics • New ILP model and method to calculate route-cost statistics

  11. Network Topologies • Two families of networks: • Degree 3 • Degree 4 America Europe Germany 15 Node 20 Node 25 Node

  12. Overview • Background • 1+1 APS • DSP • Methodology & Network Families • Design Results • Practical Methods to Increase Efficiency • Conclusion

  13. Design Results • Hop-based costs • Degree 3 Networks: 1%, 1% and 7% savings relative to APS • Degree 4 Networks: 11% - 12% savings relative to APS • Distance-based costs • Degree 3 Networks: 0.3%, 2.5% and 5.5% savings relative to APS • Degree 4 Networks: 8% - 9% savings relative to APS • Here, hop-based costing gives greater reductions

  14. Overview • Background • 1+1 APS • DSP • Methodology & Network Families • Design Results • Practical Methods to Increase Efficiency • Conclusion

  15. What to do? • Small savings over 1+1 APS found • Nowhere near SBPP values. • Six areas of concern have been identified

  16. 1: Average Network Nodal Degree • Degree 4 networks have higher savings than degree 3 • Greater amounts of disjoint routes

  17. dAB = 2 A B dAB = 1 A B 2: Demand Splitting Restrictions (Magnitude) • Integer amounts of demand • Cannot be further sub-divided

  18. w1 = 4 w1 = 3 w1 = 2 A A B B A B w2 = 1 dAB = 3 s= 3 s= 4 s= 2 Cost = 6 Cost = 5 w1 = 2 A B dAB = 4 w2 = 2 s= 2 Cost = 8 Cost = 6 3: Demand Splitting Restrictions (Divisibility) APS DSP

  19. 50% reduction in spare capacity 17% further reduction in spare capacity 4: Diminishing Returns of Greater Splitting

  20. 1.5% Savings 6% Savings 5: Degree 2 Nodes and Chains • Europe: 32% (9 of 28) • Germany: 41% (7 of 17) • America: 15% (2 of 14) A Germany

  21. 5: Degree 2 Nodes and Chains Europe Germany

  22. 6: Rapid Cost Increase of Higher Order Routes • Remember this equation? • We are fighting against it • Two important ratios

  23. Degree 3 Families Degree 4 Families 100% R2/R1 = 1.95 100% R2/R1 = 1.55 6: Rapid Cost Increase of Higher Order Routes • First ratio: Rn/R1 • Approximately 600 O-D pairs in each family

  24. Degree 3 Families Degree 4 Families 100% R2/R1 = 1.95 100% R2/R1 = 1.55 50% R3/R1 = 3.55 100% R3/R1 = 2.31 1% R4/R1 = 3.55 40% R4/R1 = 3.23 0% R5/R1 = N/A 6% R5/R1 = 4.85 0% R6/R1 = N/A <1% R6/R1 = 9.10 6: Rapid Cost Increase of Higher Order Routes • First ratio: Rn/R1 • Approximately 600 O-D pairs in each family

  25. dAB = 2 A B R1 : 2 work R2 : 2 spare Cost = 2R1 + 2R2 If R3 = R1 + R2 R1 = 1 work R2 = 1 spare Cost = R1 + R2 + (R1 + R2) = 2R1 + 2R2 R3 = 1 work 6: Rapid Cost Increase of Higher Order Routes • Second ratio: Rn/(R1 + R2) • R3 Case:

  26. Degree 3 Degree 4 6: Rapid Cost Increase of Higher Order Routes • Second ratio: Rn/(R1 + R2)

  27. 6: Rapid Cost Increase of Higher Order Routes • 6 disjoint routes A B 25 Node

  28. 6: Rapid Cost Increase of Higher Order Routes • Only 2 used in optimal solution! First two routes: 400 km Third route: 410 km R3/(R1+R2) > 1 B A 25 Node

  29. Conclusion • New ILP model • Identified important factors • Higher order route costs • Degree 2 nodes • Unit capacity (single-channel) demands • Guideline for the application of DSP • 3-way splits • These are new insights that give us an understanding of what limits the attainable efficiency of DSP

  30. Questions?

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