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26th IEEE International Conference on Distributed Computing Systems (ICDCS'06)
Improving Traffic Locality in BitTorrent via Biased Neighbor Selection
Lisboa, Portugal
July 04-July 07
ISBN: 0-7695-2540-7
Ruchir Bindal, Stanford University
Pei Cao, Stanford University
William Chan, Stanford University
Jan Medved, Cisco Systems, Inc.
George Suwala, Cisco Systems, Inc.
Tony Bates, Cisco Systems, Inc.
Amy Zhang, Cisco Systems, Inc.
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.
Citation:
Ruchir Bindal, Pei Cao, William Chan, Jan Medved, George Suwala, Tony Bates, Amy Zhang, "Improving Traffic Locality in BitTorrent via Biased Neighbor Selection," icdcs, pp.66, 26th IEEE International Conference on Distributed Computing Systems (ICDCS'06), 2006
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