Enhancing reciprocity has been one of the primary motivations for the design of incentive policies in BitTorrent-like P2P systems. Reciprocity implies that peers need to contribute their bandwidth to other peers if they want to receive bandwidth in return. However, the over-provisioning that characterizes today’s BitTorrent communities and the development of many next-generation P2P systems with real-time constraints (e.g., for live and on-demand streaming) suggest that more effort can be devoted to reducing the inequity (i.e., the difference of service received) among peers, rather than only enhancing reciprocity. Inspired by this observation, in this work we analyze in detail
several incentive mechanisms that are used in BitTorrent systems, and explore several strategies that influence the balance between reciprocity and equity. Our study shows that (i) reducing inequity leads to a better overall system performance, and (ii) the behavior of seeders (i.e., peers that hold a complete copy of the file and upload it for free) influences whether reciprocity is enhanced or inequity reduced.

Peer-to-peer (p2p ) file sharing systems rely on the sharing of resources by large fractions of partic- ipants. Sharing-ratio enforcement provides a very strong incentive for peers to contribute, leading to a higher performance for all peers in the system. One way of implementing this is by banning peers that do not share enough, determined by their past behavior (i.e., reputation). Various reputation mechanisms have been designed to facilitate this, but they are centralized or not feasible in practice. In this paper, we present a secure, fully distributed mechanism for reputation management and its integration with BitTorrent. The resulting system enforces a long-term balanced sharing-ratio for all peers in a BitTor- rent file sharing network. In our system, up- and download statistics are spread among peers and used to compute a subjective reputation for each peer. We apply the maxflow algorithm to limit the effect of peers that spread false information. We present simulations that demonstrate the system’s accuracy and effectiveness. The resulting system overcomes the long-standing problem of BitTorrent’s lack of seeding incentives, without any need for centralized administration, authority, or technology.