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Improving Traffic Locality in BitTorrent via Biased Neighbor Selection
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A thorough understanding of the network impact of emerging large-scale distributed systems -- where traffic flows and what it costs -- must encompass users' behavior, the traffic they generate and the topology over which that traffic flows. In the case of BitTorrent, however, previous studies have been limited by narrow perspectives that restrict such analysis. This paper presents a comprehensive view of BitTorrent, using data from a representative set of 500,000 users sampled over a two year period, located in 169 countries and 3,150 networks. This unique perspective captures unseen trends and reveals several unexpected features of the largest peer-to-peer system. For instance, over the past year total BitTorrent traffic has increased by 12%, driven by 25% increases in per-peer hourly download volume despite a 10% decrease in the average number of online peers. We also observe stronger diurnal usage patterns and, surprisingly given the bandwidth-intensive nature of the application, a close alignment between these patterns and overall traffic. Considering the aggregated traffic across access links, this has potential implications on BitTorrent-associated costs for Internet Service Providers (ISPs). Using data from a transit ISP, we find a disproportionately large impact on a commonly used burstable (95th-percentile) billing model. Last, when examining BitTorrent traffic's paths, we find that for over half its users, most network traffic never reaches large transit networks, but is instead carried by small transit ISPs. This raises questions on the effectiveness of most in-network monitoring systems to capture trends on peer-to-peer traffic and further motivates our approach.