Abstract
Taming the Torrent: A practical approach to reducing cross-ISP traffic in P2P systems
David R. Choffnes and Fabián E. Bustamante
Department of Computer Science
Northwestern University
Evanston, IL 60201, USA
{drchoffnes,fabianb}@cs.northwestern.edu
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%).
The slides from our presentation:
As we state in the paper, data used for this study will be made available upon request to the e-mail address below. 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.
