Trading Coordination For Randomness

1. Trading Coordination For Randomness Mike Jennings, Sachin Katti, and Dina Katabi Szymon Chachulski

2. Roofnet Wireless mesh networks have high loss rates Avg. 30% loss Objective: High throughput despite lossy links

3. Use Opportunistic Routing

4. Use Opportunistic Routing R1 • Best single path  loss prob. 50% 0% 50% R2 0% 50% dst src 0% 50% R3 50% 0% R4

5. R1 0% 50% R2 0% 50% dst src 0% 50% R3 50% 0% R4 Use Opportunistic Routing • Best single path  loss prob. 50% • In opp. routing [ExOR’05], any router that hears the packet can forward it  loss prob. 0.54 = 6% Opportunistic routing promises large increase in throughput

6. But Overlap in received packets  Routers forward duplicates

7. P1 P2 P10 P1 P1 P2 P2 But Overlap in received packets  Routers forward duplicates R1 src dst R2

8. P1 P2 P10 P1 P1 P2 P2 But Overlap in received packets  Routers forward duplicates R1 src dst R2 • State-of-the-art opp. routing, ExOR imposes a globalscheduler: • Requires full coordination; every node must know who received what • Only one node transmits at a time, others listen

9. Global Scheduling? • Global coordination is too hard • One transmitter dst src

10. Global Scheduling? dst src Can we do opportunistic routing without global scheduling? • Global coordination is too hard • One transmitter You lost spatial reuse!

11. Contributions • Opportunistic routing with no global scheduler and no coordination • We use random network coding • Experiments show that randomness outperforms both current routing and ExOR

12. Go Random Eachrouter forwards random combinations of packets

13. Randomness prevents duplicates No scheduler; No coordination Simple and exploits spatial reuse Go Random Eachrouter forwards random combinations of packets P1 R1 αP1+ßP2 P2 src dst P1 R2 γP1+δP2 P2

14. P1 P1 P2 P3 P4 P4 Random Coding Benefits Multicast P1 P2 src P3 P4 dst1 dst2 dst3 P1 P2 P2 P3 P3 P4 Without coding  source retransmits all 4 packets

15. P1 P1 P2 P3 P4 P4 Random Coding Benefits Multicast Random combinations P1 8P1+5P2+P3+3P4 P2 src P3 7P1+3 P2+6 P3+ P4 P4 dst1 dst2 dst3 P1 P2 P2 P3 P3 P4 Random coding is more efficient thanglobal coordination Without coding  source retransmits all 4 packets With random coding  2 packets are sufficient

16. MORE

17. src A B dst P1 P2 P3 4 a 0 + 2 + 1 + b + c + 1 + 3 = = = P1 P1 P1 P2 P2 P2 P3 P3 P3 a,b,c 0,2,1 4,1,3 MORE • Source sends packets in batches • Forwarders keep all heard packets in a buffer • Nodes transmit linear combinations of buffered packets Can compute linear combinations and sustain high throughput! 4,1,3 4,1,3 0,2,1

18. src A B dst P1 P2 P3 a + b + c = P1 P2 P3 a,b,c 2 + 1 = 4,1,3 8,4,7 0,2,1 MORE • Source sends packets in batches • Forwarders keep all heard packets in a buffer • Nodes transmit linear combinations of buffered packets 4,1,3 4,1,3 8,4,7 0,2,1 8,4,7

19. 4 5 1 + 5 + 3 + 4 + 2 + 5 + 5 = = = P1 P1 P1 P2 P2 P2 P3 P3 P3 1,3,2 5,4,5 4,5,5 MORE • Source sends packets in batches • Forwarders keep all heard packets in a buffer • Nodes transmit linear combinations of buffered packets • Destination decodes once it receives enough combinations • Say batch is 3 packets • Destination acks batch, and source moves to next batch

20. A dst B But How Do We Get the Most Throughput? • Naïve approach transmits whenever 802.11 allows If A and B have same information, it is more efficient for B to send it Need a method to determine how much each node should send.

21. A dst B Probabilistic Forwarding

22. P1 P2 Probabilistic Forwarding e1 A e2 dst Loss rate 0% B Src Loss rate 50% e1

23. P1 P2 Probabilistic Forwarding How many packets should I forward? 50% of buffer e1 A e2 dst B ? Src e1 e1 e1

24. P1 P2 Probabilistic Forwarding Pr = 0.5 e1 A e2 dst 0% Pr = 1 B 50% Src e1 e1 Compute forwarding probabilities without coordination using loss rates

25. Can ExOR Use Probabilistic Forwarding To Remove Coordination? Pr = 0.5 Probability of duplicates is 50% P1 P1 A P2 dst Pr = 1 B • Without random coding  need to know the exact packets to forward every time • With random coding  need to know only the average amount of overlap P1 P1

26. Adapting to Short-term Dynamics • Need to balance sent information with received information • MORE triggers transmission by receptions • A node has acredit counter • Upon reception, increment the counter using forwarding probabilities • Upon transmission, decrement the counter • Source stops  No triggers  Flow is done

27. Performance

28. Experimental Setup • We implemented MORE in Linux • 20-node testbed • Compare MORE with: • Current Routing (Single Best Path) • ExOR (State-of-the-art Opportunitic Routing) • Experiment • Random source-destination pairs • Transmit 5 MB file

29. Testbed • 20-node testbed over three floors

30. Testbed • 20-node testbed over three floors Avg. loss 27%

31. Does MORE Improve Wireless Throughput? Avg. Throughput over 180 src-dst pairs [pkts/s] MORE 80 ExOR Current 40 • MORE’s throughput is • 2x better than current routing • 22% better than ExOR

32. Throughput of All Source-Destination Pairs CDF of 180 source-destination pairs Current ExOR MORE Throughput [packets/s]

33. Zoom in on the worst 10% 4x Current ExOR MORE MORE addresses dead spots Throughput [packets/s]

34. ExOR CDF MORE CDF Batch = 8 pkts Batch = 128 pkts Batch = 8 pkts Batch = 128 pkts Throughput [packets/s] Throughput [packets/s] Sensitivity to Batch Size MORE works for short flows

35. Current MORE What About Multicast? Avg. Throughput Per Destination [pkts/s] +200% +260% 100 +320% 50 MORE improves both multicast and unicast throughput 3 destinations 4 destinations 2 destinations

36. MORE for Less! • Less coordination and less rigidity • No global scheduler • More flexibility • Works on top of 802.11  enjoy spatial reuse • One framework for unicast and multicast • More throughput • 22% better than ExOR • 2x better than current routing

37. Comments?

38. Comments • Cool idea to combine network coding with opportunistic routing • Works for multiple flows and multicast! • Performance under more flows is not good • Performance improvement over ExOR is marginal – 25% • Performance issues?

39. Comments • Cool idea to combine network coding with opportunistic routing • Works for multiple flows and multicast! • Performance under more flows is not good • Performance improvement over ExOR is marginal – 25% • Performance issues? • Stop-and-wait • End-to-end recovery • Lack of rate control