Improving Traffic Locality in BitTorrent via Biased Neighbor Selection

Abstract

Peer-to-peer (P2P) applications such as BitTorrent ignore traffic costs at ISPs and generate a large amount of cross-ISP traffic. As a result, ISPs often throttle BitTorrent traffic to control the cost. In this paper, we examine a new approach to enhance BitTorrent traffic locality, biased neighbor selection, in which a peer chooses the majority, but not all, of its neighbors from peers within the same ISP. Using simulations, we show that biased neighbor selection maintains the nearly optimal performance of Bit- Torrent in a variety of environments, and fundamentally reduces the cross-ISP traffic by eliminating the traffic’s linear growth with the number of peers. Key to its performance is the rarest first piece replication algorithm used by Bit- Torrent clients. Compared with existing locality-enhancing approaches such as bandwidth limiting, gateway peers, and caching, biased neighbor selection requires no dedicated servers and scales to a large number of BitTorrent networks.