ACNS: Adaptive Complementary Neighbor Selection in Bittorrent-Like Applications

BitTorrent, one of the most popular peer-to-peer file sharing applications, accounts for a large proportion of the total Internet traffic. While its appearance benefits the content distributors and users, the traffic injected into the network backbone has become a great challenge for the ISPs. In this paper, we study traffic shaping in BitTorrent-like applications to improve traffic locality and enable fast data delivery. To this end, a piece complementary index is introduced based on the piece demand between peer nodes. Then, we propose an efficient and adaptive neighbor selection scheme (ACNS). According to ACNS, each node self-adaptively chooses the most complementary peers to connect with and download file pieces, rather than having a fixed number of outside neighbors. Our scheme can be integrated with the BitTorrent protocol by slight modifications, and requires no additional infrastructure provided by ISPs. Experimental results based on extensive simulations have shown the effectiveness of ACNS. Compared with the fixed biased neighbor selection scheme, ANCS cuts down the cross-ISP traffic by more than 31% and improves the download rate by about 15%.