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Presenter:Elaine

Maze A Hybrid P2P file sharing system Design by Networking and distributed System lab at Peking University. Presenter:Elaine. Outline. The design of Maze ( USENIX Worlds ’ 04) An Empirical Study of Free-Riding Behavior in the Maze P2P File-Sharing System (IPTPS ’ 05 )

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Presenter:Elaine

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  1. MazeA Hybrid P2P file sharing systemDesign by Networking and distributed System lab at Peking University Presenter:Elaine

  2. Outline • The design of Maze (USENIX Worlds’ 04) • An Empirical Study of Free-Riding Behavior in the Maze P2P File-Sharing System (IPTPS’05 ) • Robust Incentives via Multilevel Tit-for-tat (IPTPS’06 ) • P2P search mechanism in Maze (ACM INFOSCALE '06)

  3. What is Maze? • One of the first large scale academic P2P-file sharing system • More than 2,300,000 Registers • Peak moment: more than 100K users • Average per day:30K users • 13TB of data are exchanged daily

  4. Structure of Maze (5) Clients contcat to multiple replicas and perform “swarm download”

  5. What will central sever log? • Metadata • owner ID • file name • File type • Size • Each transaction information • User’s reputation point

  6. Specialty in Maze • Central metadata indexing and query processing • Social network • Download network • Friend network • Eventually need to reduce the dependencies upon the central server. • These friends form the bases over which we plan to add P2P search capabilities. • Maze forum • Incentive model

  7. Incentive model • Maze Points system 1. New users are initialized with 4096 points. 2. Uploads: +1.5 points per MB uploaded 3. Downloads: -1 point per MB downloaded within 100MB, -0.7 per additional MB between 100MB and 400MB, -0.4/MB between 400MB and 800MB,and -0.1 per additional MB over 800. 4. Downloads requests are ordered by T = requestTime− 3 ∗ logP, where P is a user’s point total. 5. Users with P < 512 have a download bandwidth quota of 300Kb/s.

  8. An Empirical Study of Free-Riding Behavior in the Maze P2P File-Sharing System

  9. In-depth analysis of the effectiveness of the incentive policies and how users react to them • Definition of Free-riding behavior • Peers who only download without contributing.

  10. Statistics suggest the existence of free-riding • Results are analyzed using logs from 9/28 to 10/28. • server-like users • client-like users • The ratio is 4.4:1. • We found that client-like users are responsible for 51% downloads but only 7.5% uploads. • So, a big portion free-Riders exist!

  11. Are they not capable to contribute ? Or not willing to contribute? • What factors contributes to the free-riding behavior?

  12. When a user’s point is low.. • He can do: • Legal • Contribution • Illegal • Whitewashing • Sybil’s attack

  13. The only way that the free-riders can survive the Maze system without cheating is through contribution • From 9/28~10/28, we found that the top 10% popular files account for more than 98.8% of total transfer traffic, and over half of which were downloads from the client-like user. • So they got lots of popular files! • They can easily make back their deficits provided that • The Maze system can quickly direct queries to them and • Their contents are available

  14. The central index sever is indeed a factor! • Currently, new content of a peer does not make into the index until a few days later • Adding more indexing servers. • A more complete solution is to implement the P2P searching in the future releases

  15. Do they hide the files? • The study in [6] shows that 70% of Gnutella users do not have any files to share • The average number of shared files of client-like users is 491, versus 281 of the server-likeusers • Not the case in Maze

  16. So what mainly made them become free riders? • Users with positive point changes have longer session time, on average 2.89 times more than those with negative point changes (218 minutes versus 75 minutes)

  17. Brief Summary • Points system is effective in general • But can’t avoid free-riders using account whitewashing. • Query and search mechanism, and we can accomplish it by installing P2P searching mechanism and/or increase the frequency of updating the central index. • More savvy incentive policies (e.g. encourage people to increase their online session durations and punish whitewashing behavior

  18. Robust Incentives via Multi-levelTit-for-tat

  19. ;.;. • Private History • Giving higher priority to peers with whom he has successfully downloaded • Shared History • Non-subjective • Looking at the overall contribution of a peer • subjective • Reputation is given by all other peers, weighted by the reputation of the assigning peers

  20. Existent cheating • We analyzed the complete log of all transactions in the Maze system over a one month period, more detail in [11] • Besides the expected free riding behavior and user whitewashing • User collusion • Pair-wise collusion • Spam account collusion • a form of whitewashing that allows whitewashed points to be collected at a single user

  21. The limitation of other incentive systems • Private history coverage problem. • A one month download log only enforces Tit- for-Tat to only 2% of a peer’s upload • Peer have no opinion of the requesting peer. The blind uploads bring a lot of opportunity for free-riders.

  22. Share history • The Eigentrust Algorithm • Terminology • Local trust value:cij. The opinion that peer i has of peer j, based on past experience. • Global trust value: ti. The trust that the entire system places in peer i. P2 P3 P4 P1 P5 P3 P8 P7 P1 P5

  23. The Math

  24. The Eigentrust Algorithm • Fortunately, if n is large, the trust vector ->ti will converge to the same vector for every peer i.

  25. EigenTrust problem • Also EigenTrust helps to punish colluders in Maze • But suffers from both false negatives and false positives

  26. False negatives • Leg hugger • High reputation peers randomly download from a colluder • Example • Larry is a spam account colluder that we detected. , e.g. Most of Larry’s 30GBs uploads are to other colluders • 200MB uploads to some reputable peers boosts his rank nearly 100 times Another 734KB upload to one super peer further promotes its rank to 6.6*10-5. • Larry gets a higher rank than most of peers who upload more than 30GB to legitimate users

  27. False positive • Satellite networks • Encourage peers in satellite networks download from outsede peers

  28. So, each of the above incentives has their own weakness.. • Private history • Coverage • Shared history • Non-subjective • Solve above,but • Collusion • Subjective • Solve above,but • False positive • False negative The multi-level Tit-for-tat!

  29. Designing Multi-Trust incentive • M be a N X N matrix that defines a one step Rank among peers, i.e., Mi,jis peer j’s rank from i’s perspective. • The two step rank matrix (one level indirect trust) can be expressed as M2. The entry (M2)i;j aggregates other peers one step rank to yield the rank of j from i’s perspective. • Imposes service differentiation by looking at which tier j falls into when its downloading request arrives at i. • The smaller level it belongs to, the higher priority it is given. • Within the same tier, two peers will be ranked according to their values in the matrix of that tier.

  30. a.k.a Tit-for-tat Coverage experiment • Measurement Metrics: • Trust upload ratio • which is the volume of traffic that are served by either a M friend, or a friend that is either a {M or M2} friend, over the total traffic in the system • Using one month traffic log, simulated with incentive mechanism of Multi-Trust and Tit-for-tat.

  31. Comparism to Eigentrust • First, we use the completed transactions in our one month traffic log to calculate a set of ranking values for each peer. • Next, the evaluator node extracts download requests for the following two week period, and statistically sorts the requesters into a service queue according to their local subjective ranking. Peers with lower queue positions are served first, i.e., higher ranked. • Metric • peers’ queue positions • Expected results • True colluders will have queue positions no earlier than EigenTrust, whereas local distributors will move up

  32. Colluder punishment

  33. Leg-hugger • Larry downloads from 73 peers in the two weeks following our one month traffic log • It gets high rank in the EigenTrust because of his high rank friend. • In multitrust his high reputable friend still helps him getting into 16 (22%) peers’ trust list, but overall he is punished in all the other peers.

  34. Satellite cluster • Wayne is the local distributor in one cluster. It downloads from 14 peers in the next two weeks, among them there are 11 external peers and 3 internal peers (peer 9, 10 and 13).

  35. Brief summary • Multi-trust performs no worse than EigenTrust in punishing pairwise or spam account colluders. • Meanwhile, it solves the two problems brought by EigenTrust • dropping the high rank of leg-hugger peer • rising the low rank of local distributor inside its own satellite cluster

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