You Share, I Share: Network Effects and Economic Incentives in P2P File-Sharing Systems
We study the interaction between network effects and external incentives on file sharing behavior in Peer-to-Peer (P2P) networks. Many current or envisioned P2P networks reward individuals for sharing files, via financial incentives or social recognition. Peers weigh this reward against the cost of sharing incurred when others download the shared file. As a result, if other nearby nodes share files as well, the cost to an individual node decreases. Such positive network sharing effects can be expected to increase the rate of peers who share files.
In this paper, we formulate a natural model for the network effects of sharing behavior, which we term the “demand model.” We prove that the model has desirable concavity properties, meaning that the network benefit of increasing payments decreases when the payments are already high. This result holds quite generally, for submodular objective functions on the part of the network operator.
In fact, we show a stronger result: the demand model leads to a “coverage process,” meaning that there is a distribution over graphs such that reachability under this distribution exactly captures the joint distribution of nodes which end up sharing. The existence of such distributions has advantages in simulating and estimating the performance of the system. We establish this result via a general theorem characterizing which types of models lead to coverage processes, and also show that all coverage processes possess the desirable submodular properties. We complement our theoretical results with experiments on several real-world P2P topologies. We compare our model quantitatively against more naïve models ignoring network effects. A main outcome of the experiments is that a good incentive scheme should make the reward dependent on a node’s degree in the network.
- 1.Anagnostakis, K.G., Greenwald, M.B.: Exchange-based incentive mechanisms for peer-to-peer file sharing. In: Proc. of ICDCS 2004, Washington, DC, USA, pp. 524–533. IEEE Computer Society, Los Alamitos (2004)Google Scholar
- 2.Aperjis, C., Freedman, M.J., Johari, R.: Peer-assisted content distribution with prices. In: Proc. of CONEXT, pp. 1–12. ACM, New York (2008)Google Scholar
- 5.Cheng, A., Friedman, E.: Sybilproof reputation mechanisms. In: Proc. of P2PECON 2005, pp. 128–132. ACM, New York (2005)Google Scholar
- 8.Feldman, M., Papadimitriou, C., Chuang, J., Stoica, I.: Free-riding and whitewashing in peer-to-peer systems. In: Proc. ACM PINS 2004 (2004)Google Scholar
- 12.Gummadi, K.P., Saroiu, S., Gribble, S.D.: King: Estimating latency between arbitrary internet end hosts. In: Proc. 21st ACM SIGCOMM (2002)Google Scholar
- 13.Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence in a social network. In: Proc. 9th KDD, pp. 137–146 (2003)Google Scholar
- 15.Mossel, E., Roch, S.: On the submodularity of influence in social networks. In: Proc. 38th ACM STOC, pp. 128–134 (2007)Google Scholar
- 17.Network Coordinate Research at HarvardGoogle Scholar
- 18.Parallel & Distributed Operating Systems Group at MITGoogle Scholar
- 19.Saroiu, S., Gummadi, P., Gribble, S.: A measurement study of peer-to-peer file sharing systems. In: Proc. of MMCN (2002)Google Scholar
- 20.Schelling, T.: Micromotives and Macrobehavior. Norton (1978)Google Scholar
- 21.Vishnumurthy, V., Chandrakumar, S., Sirer, E.: Karma: A secure economic framework for peer-to-peer resource sharing. In: 1st Workshop on Economics of Peer-to-Peer Systems (2003)Google Scholar
- 22.Vondrák, J.: Optimal approximation for the submodular welfare problem in the value oracle model. In: Proc. 39th ACM STOC, pp. 67–74 (2008)Google Scholar
- 23.Zhao, B.Q., Lui, J.C.S., Chiu, D.-M.: Analysis of adaptive protocols for p2p networks. In: Proc. 28th IEEE INFOCOM (2009)Google Scholar