Mitigating the Effect of Free-Riders in BitTorrent using Trusted Agents

Sherman, Alex; Stavrou, Angelos; Nieh, Jason; Stein, Clifford S.

Even though Peer-to-Peer (P2P) systems present a cost-effective and scalable solution to content distribution, most entertainment, media and software, content providers continue to rely on expensive, centralized solutions such as Content Delivery Networks. One of the main reasons is that the current P2P systems cannot guarantee reasonable performance as they depend on the willingness of users to contribute bandwidth. Moreover, even systems like BitTorrent, which employ a tit-for-tat protocol to encourage fair bandwidth exchange between users, are prone to free-riding (i.e. peers that do not upload). Our experiments on PlanetLab extend previous research (e.g. LargeViewExploit, BitTyrant) demonstrating that such selfish behavior can seriously degrade the performance of regular users in many more scenarios beyond simple free-riding: we observed an overhead of up to 430% for 80% of free-riding identities easily generated by a small set of selfish users. To mitigate the effects of selfish users, we propose a new P2P architecture that classifies peers with the help of a small number of {\em trusted nodes} that we call Trusted Auditors (TAs). TAs participate in P2P download like regular clients and detect free-riding identities by observing their neighbors' behavior. Using TAs, we can separate compliant users into a separate service pool resulting in better performance. Furthermore, we show that TAs are more effective ensuring the performance of the system than a mere increase in bandwidth capacity: for 80\% of free-riding identities a single-TA system has a 6\% download time overhead while without the TA and three times the bandwidth capacity we measure a 100\% overhead.



More About This Work

Academic Units
Computer Science
Department of Computer Science, Columbia University
Columbia University Computer Science Technical Reports, CUCS-005-08
Published Here
April 27, 2011