On Tracker Selection for Peer-to-Peer Traffic Locality

BitTorrent (BT) is an extremely successful peer-to-peer (P2P) application providing efficient file sharing over the Internet. The ever-increasing traffic among the peers has also put unprecedented pressure to Internet Service Providers (ISPs). P2P locality has therefore been widely suggested, which explores finding local resources to optimize the cross-ISP/AS traffic. However, the ISPs would fail to reduce the cross-AS traffic if they could not control the neighbor selection of their P2P subscribers. In this paper, we examine the applicability of P2P locality through real-world measurement. We find that the widely deployed load balance trackers will greatly reduce the efficiency of traffic locality. Due to peers´ random tracker selection, there is no grantee that the peers will always choose the modified trackers as we expected. To make the matter worse, some Internet trackers involve serious copyright violation and may hardly cooperate with the ISPs. Fortunately, our investigation of the AS-Tracker relationship indicates that if we carefully select the trackers during the locality deployment, most peers can still be controlled by the ISPs with relatively high probability. A machine learning based model is then proposed to quantify the similarity of trackers´ peer distribution. Our trace-based simulation shows that, the similarity value can provide useful hints to enhance P2P locality. In particular, the peers are more likely to be optimized with higher probability. Moreover, the learning of tracker similarity does not require the global knowledge of Internet trackers, which can hardly be obtained by the individual ISPs.