Improving Traffic Locality in BitTorrent via Biased Neighbor Selection

Ruchir Bindal, Pei Cao, William Chan, Jan Medval, George Suwala, Tony Bates, Amy Zhang

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 as a way 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 BitTorrent in a variety of environments, and fundamentally reduces the cross-ISP traffic by stopping it from growing linearly with the number of peers. A key reason for its performance is the rarest first piece replication algorithm used by BitTorrent 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. Furthermore, it can be combined with bandwidth limiting and caching to improve their performance further.

The paper in PDF is here.

The source code for the simulator can be found here.

News:

We are running an Internet-wide experiment with a new tracker implementing the ideas described in the paper. Interested in having your .torrent hosted by our tracker? Please contact Pei Cao.