1. Interference-aware QoS Routing (IQRouting) for Ad-Hoc Networks Rajarshi Gupta, Zhanfeng Jia, Teresa Tung, and Jean Walrand Dept of EECS, UC Berkeley Globecom 2005 St. Louis, Missouri

2. 2 EECS, UC BerkeleyNovember 2005 Battalion of Tanks Support flows with QoS Video streaming Voice calls Urgent messages DARPA sponsored SmartNets Project

3. 3 EECS, UC BerkeleyNovember 2005 Interference Wired networks Independent links Ad-hoc networks Neighbor links interfere Interference range > Transmission range For simulations Tx range = 500 m Ix range = 1 km

4. 4 EECS, UC BerkeleyNovember 2005 Interference Model Node Link Conflict

5. 5 EECS, UC BerkeleyNovember 2005 Cliques Clique= Complete Subgraph Maximal Clique is not a subset of any other clique Cliques in Conflict Graph Set of links that all interfere with each other Closely related to capacity Clique Constraints Only one link in a clique may be active at once Flows on all links in a clique must sum  1 Maximal Cliques: ABC, BCEF, CDF

6. 6 EECS, UC BerkeleyNovember 2005 Available Bandwidth Available bandwidth on a link (avlbw) Each link part of many maximal cliques Consider slack on each clique constraint Take the minimum Available bandwidth in network/path Minimum of avlbw of all links in network/path Key difference between wired and ad-hoc In wired, width of path determined by bottleneck link In ad-hoc, width determined by bottleneck clique

7. 7 EECS, UC BerkeleyNovember 2005 Bellman’s Principle of Optimality Principle states: If optimal path from S to D goes through A, then it follows optimal path from A to D (Bellman) Distributed routing algorithms hinge on this principle

8. 8 EECS, UC BerkeleyNovember 2005 Principle of Optimality in Ad-Hoc ? Widest path from node 1 to 3 is link A (F A  1) Consider widest path from node 1 to 5 Path A-D-E: F A +F D +F E  1 so capacity  1/3 Path B-C-D-E: F B +F C  1, F C +F D  1, F D +F E  1, so capacity  1/2 Does not conform with Bellman’s Principle of Optimality Hence, work with distributed heuristic algorithms

9. 9 EECS, UC BerkeleyNovember 2005 Ad-Hoc Shortest Widest Path Shortest Widest Path Choose path with largest capacity, i.e. widest path Pick shorter path when there is a tie Distributed and localized algorithm Ad-Hoc SWP Based on clique constraints Path width Key computation: path width of one-hop extended path Can be done with localized clique information Ensures distributed computation

10. 10 EECS, UC BerkeleyNovember 2005 K-Best Paths 1: [-, 1] 2: [B, 1] 3: [A, 1], [BC, ½] 4: [AD, ½], [BCD, ½] 5: [ADE, 1/3 ], [BCDE, ½] Path Capacity Algorithm is exponential

11. 11 EECS, UC BerkeleyNovember 2005 IQRouting at Source Link state protocol distributes available bandwidth information Choose five candidate paths by source routing Widest Shortest Path (WSP) WSP compliment Shortest Feasible Path (SFP) OSPF-like weighted path cost (  + used capacity) Shortest Widest Path (SWP) Use ad-hoc versions of well-known QoS routing algorithms Account for interference among neighboring links Clique constraints determine avlbw

12. 12 EECS, UC BerkeleyNovember 2005 Distributed IQRouting Candidate paths are compared using probe packets Distributed comparison across network Nodes in path use local and current clique information Probe rejected if lack of resources QoS metric accumulated along path Best candidate chosen at destination

13. 13 EECS, UC BerkeleyNovember 2005 Comparison of Path Metric Probe packets Evaluate clique capacities along path Check if clique constraints are met Accumulate path metric (e.g. minimum of avlbw on path) Look for bottleneck clique F B +F C +F D +F others  1 F D +F E +F G +F others  1 1 2 3 4 5 6 7 8 A C B D E H G

14. 14 EECS, UC BerkeleyNovember 2005 Re-routing Adapted from AQOR protocol Key idea Path:src  dest very similar to Path:dest  src Same mechanism whether failure or QoS violation Problem detected at destination Broadcast ‘route update’ back to source Route update probe packets dealt like request packet Source will switch path to reverse of path followed by chosen route update

15. 15 EECS, UC BerkeleyNovember 2005 Simulations Topology Random 100 nodes 3 km X 3 km field Transmission 500 m Interference 1 km Flows between 5 src & 5 dest nodes Note Random flow arrivals, durations By changing mean of flow arrival and duration, we alter the “load” on the network

16. 16 EECS, UC BerkeleyNovember 2005 Comparing Admission Ratios Competing algorithms Shortest Path OSPF ILP-based SFP Ad-Hoc SFP 2 flavors IQR-Width IQR-Cost Results IQR performs better

17. 17 EECS, UC BerkeleyNovember 2005 Grid 10X10 Grid Choose node pairs 7 hops apart Compare adm ratios and path length At higher load, IQR finds longer paths with greater capacity

18. 18 EECS, UC BerkeleyNovember 2005 X position in km Y position in km 0 kbps1000 kbps500 kbps Choose Source Choose DestinationClick on bar to choose flow rateRouting…

19. 19 EECS, UC BerkeleyNovember 2005 0 kbps1000 kbps500 kbps Choose Next SourceChoose DestinationClick on bar to choose flow rateRouting…

20. 20 EECS, UC BerkeleyNovember 2005

21. 21 EECS, UC BerkeleyNovember 2005 0 kbps1000 kbps500 kbps Choose Next Source Choose DestinationClick on bar to choose flow rate Flow Rejected. Insufficient Resources

22. 22 EECS, UC BerkeleyNovember 2005 Conclusions Multi-hop services have a long way to go Actual capacity far lower than advertised Shortest path methods are inadequate Heuristic schemes most promising IQRouting proposes one simple, distributed algorithm for ad-hoc networks Performance results show significant improvement

23. Questions http://www.eecs.berkeley.edu/~guptar guptar@eecs.berkeley.edu