The Performance and Locality Tradeoff in BitTorrent-Like P2P File-Sharing Systems

The recent surge of large-scale peer-to-peer (P2P) applications has brought huge amounts of P2P traffic, which significantly changes the Internet traffic pattern and increases the traffic-relay cost at the Internet Service Providers (ISPs). To alleviate the stress on networks, localized peer selection has been proposed that advocates neighbor selection within the same network (AS or ISP) to reduce the cross-ISP traffic. Nevertheless, localized peer selection may potentially lead to the downgrade of downloading speed at the peers, rendering a non-negligible tradeoff between the downloading performance and traffic localization in the P2P system. Aiming at effective peer selection strategies that achieve any desired Pareto optimum in face of the tradeoff, in this paper, we characterize the performance and locality tradeoff as a multi-objective b-matching optimization problem. In particular, we first present a generic maximum weight b-matching model that characterizes the tit-for-tat in BitTorrent-like peer selection. We then introduce multiple optimization objectives into the model, which effectively characterize the performance and locality tradeoff using simultaneous objectives to optimize. We also design fully distributed peer selection algorithms that can effectively achieve any desired Pareto optimum of the global multi-objective optimization, that represents a desired tradeoff point between performance and locality in the entire system. Our models and algorithms are supported by rigorous analysis and extensive simulations.