Adaptive Peer Selection

Daniel S. Bernstein, Zhengzhu Feng, Brian Neil Levine, and Shlomo Zilberstein. Adaptive Peer Selection. Proceedings of the Second International Workshop on Peer-to-Peer Systems, 237-246, Berkeley, California, 2003.

Abstract

In a peer-to-peer file-sharing system, a client desiring a particular file must choose a source from which to download. The problem of selecting a good data source is difficult because some peers may not be encountered more than once, and many peers are on low-bandwidth connections. Despite these facts, information obtained about peers just prior to the download can help guide peer selection. A client can gain additional time savings by aborting bad download attempts until an acceptable peer is discovered. We denote as peer selection the entire process of switching among peers and finally settling on one. Our main contribution is to use the methodology of machine learning for the construction of good peer selection strategies from past experience. Decision tree learning is used for rating peers based on low-cost information, and Markov decision processes are used for deriving a policy for switching among peers. Preliminary results with the Gnutella network demonstrate the promise of this approach.

Bibtex entry:

@inproceedings{BFLZiptps03,
  author	= {Daniel S. Bernstein and Zhengzhu Feng and Brian Neil Levine and
                   Shlomo Zilberstein},
  title		= {Adaptive Peer Selection},
  booktitle     = {Proceedings of the Second International Workshop on Peer-to-Peer
                   Systems},
  year		= {2003},
  pages		= {237-246},
  address       = {Berkeley, California},
  url		= {http://rbr.cs.umass.edu/shlomo/papers/BFLZiptps03.html}
}

shlomo@cs.umass.edu