Buddynet: History-based P2P search

Peer-to-peer file sharing has become a very popular Internet application. P2P systems such as Gnutella and Kazaa work well when the number of peers is small. Their performances degraded significantly when the number of peers scales. In order to overcome the scalability problem, numerous research groups have experimented with different approaches. We conduct a novel evaluation study on Kazaa traffic focusing on the interest-based locality. Our analysis shows that strong interest-based locality exist in P2P systems and can be exploited to improve performance. Based on our findings, we propose a history-based P2P search algorithm and topology adaptation mechanism. The resulting system naturally clusters peers with similar interests to each other and greatly improves the efficiency for searching. We test our design through simulations; the results show significant reduction in total system load and large speedup in search efficiency compared to random walk and interest shortcut schemes. In addition, we show that our system is more robust under dynamic situations.