1. Roadmap-Based End-to-End Traffic Engineering for Multi-hop Wireless Networks Mustafa O. Kilavuz Ahmet Soran Murat Yuksel University of Nevada Reno

2. Outline Introduction Framework Simulation Results Conclusion and future work

3. Introduction

4. Motivation Why load balance the traffic (i.e., traffic engineering) in multi-hop wireless networks? – Mitigate hotspots – Attain higher throughput (aggregate throughput is maxed) – Lifetime of the network (load on nodes/routers is evenly distributed)

5. Desired Properties Flexible: End-to-end route selection capability (like MPLS) – Source application can control paths the traffic takes Scalable: Do not want to store – Global topology information – Flow state Can we achieve both by in a feasible and scalable manner?

6. Flexibility: Source-Based E2E Trajectories Defining E2E paths require topology info – hard to get Idea: Decouple the E2E path from the underlying topology, control plane costs could be reduced significantly! Ideal Trajectory Approximate Trajectory Actual Trajectory

7. Void Area Trajectory-Based Forwarding (TBF) Source Destination Data D. Niculescu B. Nath

8. Scalability: Roadmaps Need to summarize the congestion state of the global network – hard to gather Idea: Use the adaptive roadmaps concept from robotics S. Bhattacharya, et al

9. Current Schemes Mostly shortest path – Greedy – Not suitable for load balancing – E.g. GPSR Mostly topology dependent – Not scalable against network changes/dynamics – E.g. DSR

10. Overall Framework

11. Routing Framework with Roadmap Roadmap Trajectory Approximator Application-Specific Constraint (e.g., path accuracy, max delay) send(dest, data, constraint) Network Packets with approximate trajectory to the network send(dest, apprx_traj, data) Congestion indications as link weight updates to the roadmap Shortest path on the roadmap as ideal trajectory Path Selection for E2E TE at Routing Layer

12. Void Area Building the Roadmap

13. Generating Ideal Trajectory Void Area Source Destination

14. Feedback: Void Areas Void Area Source Destination Data Feedback

15. Feedback: Congested Areas Congestion causes packet drops Broadcast feedback – High priority – Small size 50% probability to reroute

16. Load Balancing Roadmap edge weights are increased as they are being used. Unused edges’ weights are gradually decreased. Change trajectory after sending n packets over it.

17. Simulation

18. Simulation Setup Goal: Maximum throughput TBR vs. Greedy Perimeter Stateless Routing (GPSR) Why GPSR? – Similar properties with TBR Geographic Scalable Topology-independent – Good reference for benchmarking Shortest path No end-to-end

19. Void Area Greedy Perimeter Stateless Routing (GPSR) Source Destination Greedy Forwarding Perimeter Forwarding Greedy Forwarding B. Karp, H. Kung

20. Simulation Setup Field size1500 x 1500 pixel 2 Wireless node range150 pixels Runtime20s Traffic rate160 Kbps Network density10, 15, 20, 25 – Number of nodes114, 171, 229, 286 Number of traffic flows3, 5, 10 Packet queue size5, 10, …, 50 Reruns16

21. Simulation: Trajectories Source Destination

22. Simulation: Roadmap

24. Results

25. Work Load Heat Map GPSR Roadmap based TBR

26. Throughput QDFQDF Q – Packet queue size of nodes D – Network density (Average number of neighbors) F – Number of traffic flows (Source – destination pairs) TBR has higher throughput overall GPSR has good throughput on sparse networks High number of flows increases congestion, reduces throughput High queue size increases throughput

27. Hop Count QDFQDF Q – Packet queue size of nodes D – Network density (Average number of neighbors) F – Number of traffic flows (Source – destination pairs) TBR has longer routes to avoid congestion and to do load balancing Network density is not a major factor but causes GPSR spikes because of perimeter mode

28. Packet Delay QDFQDF Q – Packet queue size of nodes D – Network density (Average number of neighbors) F – Number of traffic flows (Source – destination pairs) TBR packet delay increases within acceptable amounts Large queue size causes more delay

29. Conclusion and Future Work

30. Conclusion Mobile scenarios Algorithms optimization Improvements to roadmaps – Construction (regular patterns) – Better methods for ideal trajectory – Local vs. global

31. Questions & Answers

32. Backup Slides

33. Void Area Trajectory-Based Routing (TBR) Data Source Destination M. Yuksel et al. Ideal Trajectory Approximate Trajectory Special Intermediate Node (SIN)

34. Work Load distribution QDFQDF Q – Packet queue size of nodes D – Network density (Average number of neighbors) F – Number of traffic flows (Source – destination pairs) TBR distributes load better Load is more balanced in dense networks High number of flows puts more load on central nodes

35. Contributions The concept of minimizing routing state under application-based constraints. Formulation of the trajectory approximation problem minimizing the routing state. Proof that the trajectory approximation problem is NP- hard. Solutions to solve the trajectory approximation problem. Customized the trajectory approximation problem for power-scarce networks. A roadmap-based mechanism for end-to-end traffic engineering for multi-hop wireless networks.

36. Roadmap Simulations Source Node Destination Node Void Area Data Packet Approximate Trajectory

37. Roadmap Simulations Roadmap Edges Ideal Trajectory Roadmap Vertices Source Node Destination Node Approximate Trajectory

38. Roadmap Simulations

40. Routing Protocols Destination-Sequenced Distance Vector (DSDV) Ad hoc On Demand Distance Vector (AODV) Greedy Perimeter Stateless Routing (GPSR) Distance Routing Effect Algorithm for Mobility (DREAM) Dynamic Source Routing (DSR) Trajectory-Based Forwarding (TBF) Trajectory-Based Routing (TBR) Roadmaps in robotics

41. Destination-Sequenced Distance Vector (DSDV) 22 33 43 53 63 73 1 2 3 4 6 7 5 14 24 34 44 64 77 16 26 36 46 66 55 14 24 34 44 54 77 13 22 33 55 66 75 11 34 44 54 64 74 11 24 33 54 64 74 Destination Next hop 22 33 43 53 63 73 11 24 33 54 64 74 13 22 33 55 66 75 14 24 34 44 64 77 Source Destination Routing table

42. Ad hoc On Demand Distance Vector (AODV) 1 2 3 4 6 7 5 RREQ Source Destination RREP 52 54 55

43. Distance Routing Effect Algorithm for Mobility (DREAM) Source Destination

44. Dynamic Source Routing (DSR) 1 2 3 4 6 7 5 1 1 1 | 2 1 | 3 1 | 2 | 4 1 | 2 | 4 | 6 1 | 2 | 4 | 6 | 7 Source Destination 1 | 2 | 4 | 5 1 | 2 | 4 | Data 1 | 2 | 4 | 6 | 7 | 5

45. Comparison DSDVAODVGPSRDREAMDSRTBR Flexibility Scalability (State) Scalability (Messaging) Reachability Computation Type ProactiveReactive        

46. Cost Comparison Error tolerance: 5% GA performs pretty close to the exhaustive search Longest representation heuristic is not bad Exhaustive Search Equal error heuristic did not do well Equal Error Longest Representation M. Kilavuz et. al. Minimizing multi-hop wireless routing state under application-based accuracy constraints, MASS 2008

47. Equal Error Longest Representation Time Comparison Equal Error heuristic runs in no time Exhaustive search takes too much time These run in reasonable amount of time Error tolerance: 5% M. Kilavuz et. al. Minimizing multi-hop wireless routing state under application-based accuracy constraints, MASS 2008