Centralised solutions for Video-on-Demand (VoD) services, which stream pre-recorded video content to multiple clients who start watching at the moments of their own choosing, are not scalable because of the high bandwidth requirements of the central video servers. Peer-to-peer (P2P) techniques which let the clients distribute the video content among themselves, can be used to alleviate this problem. However, such techniques may introduce the problem of free-riding, with some peers in the P2P network not forwarding the video content to others if there is no incentive to do so. When the P2P network contains too many free-riders, an increasing number of the well-behaving peers may not achieve high enough download speeds to maintain an acceptable service. In this paper we propose Give-to-Get, a P2P VoD algorithm which discourages free-riding by letting peers favour uploading to other peers who have proven to be good uploaders. As a consequence, free-riders are only tolerated as long as there is spare capacity in the system. Our simulations show that even if 20% of the peers are free-riders, Give-to-Get continues to provide good performance to the well-behaving peers. In particular, they show that Give-to-Get performs very well for short videos, which dominate the current VoD traffic on the Internet.

A well-known problem in P2P systems is freeriding, where users do not share content if there is no incentive to do so. In this paper, we distinguish lazy freeriders that are merely reluctant to share but follow the protocol, versus die-hard freeriders that employ sophisticated methods to subvert the protocol. Existing incentive designs often provide theoretically attractive resistance against die-hard freeriding, yet are rarely deployed in real networks because of practical infeasibility. Meanwhile, real communities benefit greatly from prevention of lazy freeriding, but have only centralized technology available to do so. We present a lightweight, fully distributed mechanism called BARTERCAST that prevents lazy freeriding and is deployed in practice. BarterCast uses a maxflow reputation algorithm based on a peer's private history of its data exchanges as well as indirect information received from other peers. We assess different reputation policies under realistic, trace-based community conditions and show that our mechanism is consistent and effective, even when significant fractions of peers spread false information. Furthermore, we present results of the deployment of BarterCast in the BitTorrent-based Tribler network which currently has thousands of users worldwide.

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Abstract-Most P2P systems that have some kind of incentive mechanism reward peers according to their contribution, i.e. total bandwidth offered to the system. Due to the disparity in bandwidth capacity between P2P users on the Internet, the common effect of such mechanisms is that the fastest peers reap the highest benefits. We take a different approach and study how to incentivize cooperation in P2P systems based on effort, i.e. contribution relative to capacity. We make the following contributions: 1) we argue that contribution-based incentive schemes in P2P systems unnecessarily disfavor slow peers and decrease overall system performance; 2) we advocate the use of principles from an alternative economic vision, Participatory Economics (Parecon), to inspire systems to be fair and to ensure maximization of the social welfare while being efficient at the same time, and 3) we present the results of simulations in which we apply principles from Parecon to two popular real life systems: a) the popular file sharing BitTorrent protocol; b) a generic credit based sharing ratio enforcement scheme. Our approach yields a higher system performance and fairness and offers new insights into P2P incentive design.

BitTorrent is a highly popular peer-to-peer filesharing protocol. Much BitTorrent activity takes place within private virtual communities called "Private Trackers" -a server that allows only community members to share files. Many private trackers implement "ratio enforcement" where the tracker monitors the upload and download behaviour of peers. If a peer downloads substantially more than it uploads then service is terminated. Tracker policies related to credit effect the performance of the community as a whole. We identify the possibility of a "credit squeeze" in which performance is reduced due to lack of credit for some peers. We consider statistics from a popular private tracker and results from a simple model (called "BitCrunch").