Taming the Torrent: A practical approach to reducing cross-ISP traffic in P2P systems
David R. Choffnes and Fabián E. Bustamante
In Proc. of ACM SIGCOMM, August 2008.
EECS Department
Northwestern University
Evanston, IL 60201, USA
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Abstract
Peer-to-peer (P2P) systems, which provide a variety of popular services, such as file sharing, video streaming and voice-over-IP, contribute a significant portion of today's Internet traffic. By building overlay networks that are oblivious to the underlying Internet topology and routing, these systems have become one of the greatest trafficengineering challenges for Internet Service Providers (ISPs) and the source of costly data traffic flows. In an attempt to reduce these operational costs, ISPs have tried to shape, block or otherwise limit P2P traffic, much to the chagrin of their subscribers, who consistently finds ways to eschew these controls or simply switch providers.
In this paper, we present the design, deployment and evaluation of an approach to reducing this costly cross- ISP traffic without sacrificing system performance. Our approach recycles network views gathered at low cost from content distribution networks to drive biased neighbor selection without any path monitoring or probing. Using results collected from a deployment in BitTorrent with over 120,000 users in nearly 3,000 networks, we show that our lightweight approach significantly reduces cross-ISP traffic and over 33% of the time it selects peers along paths that are within a single autonomous system (AS). Further, we find that our system locates peers along paths that have two orders of magnitude lower latency and 30% lower loss rates than those picked at random, and that these highquality paths can lead to significant improvements in transfer rates. In challenged settings where peers are overloaded in terms of available bandwidth, our approach provides 31% average download-rate improvement; in environments with large available bandwidth, it increases download rates by 207% on average (and improves median rates by 883%).
Data Set
As we state in the paper, data used for this study will be made available upon request to This email address is being protected from spambots. You need JavaScript enabled to view it. . For privacy reasons, the data is provided at an AS-level granularity. Note that you will have to agree to these terms before we grant access to the data. Also note that the dataset consists of 10s of GB of compressed data, so plan accordingly. For more details, see the Edgescope project page.
