1 / 37

Trading Coordination For Randomness*

Trading Coordination For Randomness*. A. Zubow. “Trading Structure for Randomness in Wireless Opportunistic Routing”, S. Chachulski, M. Jennings, S. Katti and D. Katabi, SIGCOMM’07, nms.csail.mit.edu/~dina/pub/MORE.pdf. Roofnet. Wireless m esh networks have high loss rates.

satherr
Download Presentation

Trading Coordination For Randomness*

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Trading Coordination For Randomness* A. Zubow “Trading Structure for Randomness in Wireless Opportunistic Routing”, S. Chachulski, M. Jennings, S. Katti and D. Katabi, SIGCOMM’07, nms.csail.mit.edu/~dina/pub/MORE.pdf

  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 Does opportunistic routing have to be so complicated? • Global coordination is too hard • One transmitter You lost spatial reuse!

  11. Our 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 Our Madness

  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 exactpackets to forward every time • With random coding  need to know only the averageamount of overlap P1 P1

  26. There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don't know. But there are also unknown unknowns. Long-term averages are great, but… Wireless is unpredictable over short time-scales MORE needs to adapt to short-term dynamics

  27. 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

  28. Performance

  29. 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

  30. Testbed • 20-node testbed over three floors

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

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

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

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

  35. 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

  36. 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

  37. MORE for Less! • Lesser coordination and lesser rigidity • No 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

More Related