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Robust Incentives via Multi-level Tit-for-tat

IPTPS, Feb. 2006. Robust Incentives via Multi-level Tit-for-tat. Qiao Lian , Zheng Zhang (MSRA) Yu Peng, Mao Yang, Yafei Dai, Xiaoming Li (PKU). P2P file-sharing needs incentives to work. genuine incentives : must collaborate/share to benefit E.g. block exchange in BT

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Robust Incentives via Multi-level Tit-for-tat

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  1. IPTPS, Feb. 2006 Robust Incentives via Multi-level Tit-for-tat Qiao Lian, Zheng Zhang (MSRA) Yu Peng, Mao Yang, Yafei Dai, Xiaoming Li (PKU)

  2. P2P file-sharing needs incentives to work • genuine incentives: must collaborate/share to benefit • E.g. block exchange in BT • Problems: only works within a large session • Nearly 80% sessions contain 2 peers only, i.e. there is only one downloader • No one else to collaborate with!

  3. A simple breakdown of the spectrum • Artificial incentive: • Produce/record evidence of collaboration for future reference Brittle to collusion and other problems Incentives Shared history Private history Artificial incentives genuine incentives subjective Non-subjective Absolute contribution (e.g. Maze) The sum of contribution from the perspective of other peers, weighted by their reputation (e.g. EigenTrust)

  4. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  5. Maze:architecture • A sends query • server responses with file / replica info • A sends download requests • B and C response with file data • B and C upload traffic log centralize maintained index / membership user cloud Our work starts from these logs C A B

  6. Vital statistics • Popular • Population: 1.4 million registered accounts; 30,000+ online users • More than 200 million files • More than 13TB (!) transfer everyday • Completely developed, operated and deployed by an academic team • Logs added since the collaboration w/ MSRA in 2004 • Enable detailed study at all angles

  7. Maze：Incentive Policies • New users: points == 4096 • Point change: • Uploads: +1.5 points per/MB • Downloads: at most -1.0 point/MB • Gives user more motivation to contribute • Benefit of high point • Climbing ladder  social status • Service differentiation: • Order download requests by T = Now – 3log(Point) • Users with P < 512 have a download bandwidth of 200Kb/s • Available in Maze5.0.3; extensively discussed in Maze forum before implemented

  8. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  9. What is collusion • Definition (Webster dictionary): • secret agreement or cooperation especially for an illegal or deceitful purpose • And in the Maze context: • Multiple peers collude to defeat the incentive system • What makes the study hard: • Even with all the traffic logs, we will never know for sure • But we can identify suspicious colluding patterns • See our technical report for more details

  10. the collusion workingset 221,000 pairs whose duplication degree > 1 the top 100 pairs with most redundant traffic • Repeat traffic detector • Hint: colluders are lazy • for peer pair link: duplication degree = total traffic / unique data

  11. A closer look… Ted: 3.8TB Sam: 47GB Ingrid: 78GB Mary: 73GB Star-shape collusion(spam account): colluding + whitewashing account (Fred, Gary) (Olga, Pam) Pair-wise collusion (David, Alice, Quincy) e.g. Alice uploads MSDN DVD image (~3GB) for 29 times (Harry, Cindy)

  12. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  13. What about EigenTrust? • EigenTrust: clone of PageRank • Basic idea: • Consider recommender’s reputation • Trust matrix M: • mi,j: trust of peer i to peer j (e.g download quantity) • normalize each row of M: • EigenTrust vector: • The left principal eigenvector T • The rank of peer i is Ti

  14. What about EigenTrust? A 9GB 9GB 1GB 10GB B • EigenTrust: clone of PageRank • Basic idea: • Consider recommender’s reputation • Trust matrix M: • mi,j: trust of peer i to peer j (e.g download quantity) • normalize each row of M: • EigenTrust vector: • The left principal eigenvector T • The rank of peer i is Ti 1GB C 10GB 30GB

  15. False negative of EigenTrust How the leg-hugger has high score: leg-hugger Larry • Does the 734KB upload to Ted really matter? • No, Ted is an irrational user • It downloads only 124MB, but uploads 3.8TB.

  16. False positive of EigenTrust(local distributor Wayne) 5600GB Local distributor Wayne • Wayne is in a satellite cluster • Wayne uploads 290GB. • Its EigenRank equals to a peer in majority community with 10GB upload • Is it fair? • At least, Wayne should have high rank inside the satellite cluster. • We need personalized rank for each peer, e.g. Tit-for-Tat

  17. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  18. Private history: Tit-for-Tat • Idea: trust peers (friends) who has helped me before • Used in eMule and BitTorrent (the 2 popular P2P filesharing system)

  19. Private history: Tit-for-Tat • Idea: trust peers (friends) who has helped me before • Used in eMule and BitTorrent (the 2 popular P2P filesharing system) • Problem: extremely small coverage ???? Limited coverage even with longer history

  20. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  21. Multi-trust incentive algorithm • Idea: we need more than one tier of trust! • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  22. Multi-trust incentive algorithm • Idea: we need more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  23. Multi-trust incentive algorithm • Idea: we need more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  24. Multi-trust incentive algorithm • Idea: we need more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list

  25. Multi-trust incentive algorithm • Idea: needs more than one tier of trust • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list A B C D 1-hop friends 2-hop friends E 3-hop friends F other peers …… F A B E C D

  26. Multi-trust incentive algorithm • Idea: needs more than one tier of trust Mathematically answer: use full spectrum { M, M2, … M∞ } • get friends’ 1-hop friends • build friends’ friend list, i.e., 2-hop friend list • get friends’ 2-hop friends • build 3-hop friend list M M2 M3

  27. Multi-trust incentive algorithm multi-trust: the full spectrum incentive algorithm • Evaluation: • Coverage: real trace driven simulation of one month • Effectiveness: statically evaluate the next 2 weeks traffic • Metric: colluder’s queue position at the data source peer Tit-for-Tat M∞T: EigenTrust { M, M2, … M∞ } Coverage Personalization

  28. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  29. Multi-trust incentive algorithmCoverage experiment • The coverage of {M, M2} is already good enough • We can choose using {M, M2, M∞}

  30. Multi-trust incentive algorithm: Effectiveness expr. methodology • Setup • Generating rank based on one months history • Evaluate the next two weeks • Metric: • We don’t have a global rank … • Queue Position at each source peer: • Source peers: who holds interested resource to me

  31. Multi-trust incentive algorithm: dealing with colluders Spam account colluder • 5/7 punish Ingrid equally • Peer 7 punishes more in multi-trust • Peer 4 punishes less in multi-trust since it downs from Ingrid Desirable: as good as EigenTrust Pair-wise colluder • 7/9 punish Cindy equally • 2/9 punish more in multi-trust • Friends get ahead!

  32. Multi-trust incentive algorithm:solve problems in EigenTrust False-negative False-positive Leg-hugger • 78% peers rank Larry lower • 22% are still affect by super peers Ted. Local distributor • Inside: • 2/3 peers promote Wayne’s rank • 1/3 is too young to know Wayne’s good • Outside: another friend

  33. Talk organization • The Maze p2p file sharing system • Existing collusion behaviors • Why simple algorithms do not work • EigenTrust and Tit-for-Tat • Multi-trust algorithm • Evaluation • Summary and Related work • Conclusion

  34. Summary and Related Work

  35. Conclusion • EigenTrust and Tit-for-tat each have their own pitfall • Multi-trust as a hybrid achieves betterbalance

  36. Thank you Q&A

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