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Yitzchak Rosenthal

P2P Mechanism Design: Incentives in Peer-to-Peer Systems Paper By: Moshe Babaioff , John Chuang and Michal Feldman. Yitzchak Rosenthal. Types of P2P networks. P2P network applications File downloading (e.g. BitTorrent, Gnutella, etc.) Video streaming MANETs. P2P Issues.

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Yitzchak Rosenthal

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  1. P2P Mechanism Design: Incentives in Peer-to-Peer SystemsPaper By: Moshe Babaioff, John Chuang and Michal Feldman Yitzchak Rosenthal

  2. Types of P2P networks • P2P network applications • File downloading (e.g. BitTorrent, Gnutella, etc.) • Video streaming • MANETs

  3. P2P Issues • Private Information • Many P2P protocols require clients to divulge “private information”.Examples: • Amount of bandwidth a client has for uploading files. • List of files/data client has for uploading • Clients may choose NOT to divulge private information in order to exploit the network for its own gain. • Free Riding • Peers try to get use OF network without providing services TO network(e.g. downloading data from peers without uploading to peers) • Whitewashing • If multiple identities can be created for free then an “evil” user can destroy an identity once it has been recognized as not following the rules and exploiting the network • Sybil Attacks • Multiple IDs by same user that collude with each other

  4. Addressing the problem through “Incentives” • Provide “incentives” to peers to follow the rules • Types of incentives • Currency (CUR) - Mojonation • Peers earn “currency” when providing TO the network. • The “currency” can be “spent” in order to get services/data FROM the network • Reputation (REP) - KaZaA • Peers get a better reputation when they provide TO the network • Peers with better reputation get better download speed • Barter (BAR) - BitTorrent • Scalable - doesn’t keep state information (CUR and REP do) • Files are broken into many equal size chunks • “seeder” peer distributes DIFFERENT chunks to many different peers • Peers who have a chunk exchange with peers who have other chunks.

  5. Case study: File Sharing Networks

  6. One shot game • In a ONE SHOT GAME - free Riding is the dominant strategy • Similar to one shot Prisoner’s Dilemma (PD) where dominant strategy is to defect. • No downsides for cheating • No loss of reputation • No way to spend any “income”

  7. Other approaches • Direct reciprocity can be better, but • In large population, effect of direct reciprocity is diluted since the odds of interacting again with same peer is low (Friedman, et al) (See next slide) • Enforce direct interaction with limited number of peers (BitTorrent) • Reputation systems – introduces state – may not scale as well • How to deal with newcomers: • Dissuade whitewashing by • Cooperate with strangers with a fixed probability, p, is not robust against white washers • Better approach is adjust p based on frequency of past cooperation with strangers. This works better for a small turnover rate.

  8. Dilution of effects of direct reciprocity with large population.

  9. Reputation

  10. Reputation • Areas that reputation work: • Evolutionary biology • Online marketplaces (e.g. eBay) • FileSharing - eg. KaAzA – files who upload have better reputation scores and get higher priority when downloading • Eigentrust algorithm • Uses “transitive trust relationships” to aggregates local “trust values” to form “global” trust values • Similar to “page rank” in Google • Credence algorithm • Extends “trust” from peers to objects in p2p system to defend against pollution and poisoning

  11. Minimalist P2P model (no reputation) • Each peer i has type θ = generosity = amount that peer will contribute to system • x = # of contributors to system • Contribution cost per peer = 1/x • Decision of rational peer: • See graph on next slide

  12. Miminimalist P2P model - costs • Y axis is # of contributors to system • X axis is generosity level • x1, x2 (on Y axis) and zero are equilibria of system • X2 is NOT a stable equilibrium • Generosity θis uniformly distributed beween 0 and θm. • Straight line is CDF of percent of peers who will contribute at a certain price level. • Curved line is the model of the cost per contributor.

  13. Solve for x1 and x2 • Solve for

  14. Benefits • Benefit proportional to contribution level – α • Performance of System: • Ws = αx – (1/x)x = αx -1 (note 1/x is used instead of 1/ θ ) • System will still collapse if maximal generosity is low

  15. Reputation system • Catch free riders with probabilty, p, an eliminate free riders from systemORcatch free riders with probability 1 and peanalize free riders with (1-p) times reduced service of contributor • Load placed on system decreases to : • So contribution cost becomes:

  16. Analysis with reputation • Q – individual benefit • R – reduced contribution cost • T – threat • Contributor performance : Q – R = • Free Rider performance : • System Performance:

  17. Analysis of Barter Based System (BitTorrent)

  18. Principal Agent Model • N – set of agents • n : # of agents • Ai = {0,1} : set of possible actions for each agent, i ∈ N • – a specific agent • Set of n agents, N

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