In this paper, we propose a hierarchical architecture for grouping peers into clusters in a large-scale BitTorrent-like underlying overlay network in such a way that clusters are evenly distributed and that the peers within are relatively close together. We achieve this by constructing the CBT (Clustered BitTorrent) system with two novel algorithms: a peer joining algorithm and a super-peer selection algorithm. Proximity and distribution are determined by the measurement of distances among peers. Performance evaluations demonstrate that the new architecture achieves better results than a randomly organized BitTorrent network, improving the system scalability and efficiency while retaining the robustness and incentives of original BitTorrent paradigm. © IFIP International Federation for Information Processing 2006.
CITATION STYLE
Xiao, B., Yu, J., Shao, Z., & Li, M. (2006). Distributed proximity-aware peer clustering in bit torrent-like peer-to-peer networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4096 LNCS, pp. 375–384). Springer Verlag. https://doi.org/10.1007/11802167_39
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