1. A Practical Approach to QoS Routing for Wireless Networks Teresa Tung, Zhanfeng Jia, Jean Walrand WiOpt 2005—Riva Del Garda

2. Outline Problem: clustering Assumptions: routing algorithm Analysis: simple models Analysis: simulations

3. Scenario Routing over ad-hoc wireless networks Goal: Discover the diverse paths Small area, use shortest path Uniform demand, shortest path admits most flows Demand between few s-d pairs, use diverse paths to increase capacity

4. Observation on Interference Interference –Area effect –Not a link effect Routing choices –Over areas –Not over links TxIntfx

5. Related Work Theoretical Approach Gupta Kumar Thiran Practical Fixed transmission radius Routing algorithms

6. Clustering: Motivation Clustering makes sense for dense networks Each node sees roughly the same info

7. Clustering: Motivation Clustering makes sense for dense networks Each node sees roughly the same info

8. Clustering: Motivation Clustering makes sense for dense networks Each node sees roughly the same info

9. Clustering: Motivation Clustering makes sense for dense networks Each node sees roughly the same info

10. Costs Cost of flat routing –No point in all nodes reporting –Reduction in control messages –Limited loss of information Cost of clustering –Restrict possible paths –Use more network resources

11. Outline Problem: clustering Assumptions: routing algorithm Analysis: simple models Analysis: simulations

12. Routing granularity Comparison of routing strategies over a flat network shows little improvement Scheme –Shortest path within clusters –OSPF at the cluster level –Measurement –Admission Control

13. Routing Source Dest

14. Routing

15. Routing: Measurement Measure the available resources in a cluster Use a representative node per cluster Given the link speed Measure the fraction of time that the channel is busy –Transmitting/Receiving –Channel busy The fraction of idle time x link speed gives an upper bound on residual capacity

16. Routing: OSPF weights Estimate residual capacity Shortest feasible path Most probable path Residual capacity

17. Routing: Admission Control For inelastic flows require a rate F Trial flow of same rate F for period t Trial packets served with lower priority Admit if all trial packets received Otherwise busy 802.11e Admitted Trial high

18. Routing Assumptions Shortest path within clusters Resource estimates via measurements OSPF based scheme at the cluster level Admission control

19. Outline Problem: clustering Assumptions: routing algorithm Analysis: simple models Analysis: simulations

20. Clustering: Analysis Model Continuous plane (dense network) Compare routes over an idle network Grid clustered Compare –Length –Self interference –Diversity

21. Compare # hops Clustering: Length

22. Path length: grid size

23. Path length: grid = 2r

24. Clustering: Self-Interference Unit disk model, interference radius Self-interference for shortest path

25. Clustering: Self-Interference Midpoint on II –From II –From I and III each Decreasing in grid size

26. Clustering: path diversity

27. Cost of Flat Routing N nodes over area A=ar x ar where r tx radius C=(a/g)^2 clusters of size gr x gr Average hops between nodes L Average hops across cluster < gsqrt2 Flat routing LN 2 Clustered routing (gc1+c2L)C 2

28. Outline Problem: clustering Assumptions: routing algorithm Analysis: simple models Analysis: simulations

29. Outline Problem Argument for clustering Routing scheme Simulation results

30. Simulations Matlab Algorithms Global OSPF Event driven OSPF Event+clustered OSPF 100 nodes, vary density Mesh topology (5x5) Random topology (3x3,4x4)

31. Clustering: Shortest Path

32. Simulations: Admission Ratio Mesh over a 5x5 Grid Random over a 3x3 Grid

33. Simulations: Max capacity s-d Mesh over a 5x5 Grid Random over a 3x3 Grid

34. Simulations: Average path length Mesh over a 5x5 Grid Random over a 3x3 Grid

35. Simulations: Path length for fixed s-d pair

36. Simulations: Path Diversity

37. Simulations: ave # routes s-d Mesh over a 5x5 Grid Random over a 3x3 Grid

38. Conclusion Cost of clustering: 20% loss in admit ratio Path length Self-interference Path diversity www-inst.eecs.berkeley.edu/~teresat teresat@eecs.berkeley.edu