Distributed proximity-aware peer clustering in bit torrent-like peer-to-peer networks

Bin Xiao, Jiadi Yu, Zili Shao, Minglu Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

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.
Original languageEnglish
Title of host publicationEmbedded and Ubiquitous Computing - International Conference, EUC 2006, Proceedings
PublisherSpringer Verlag
Pages375-384
Number of pages10
ISBN (Print)3540366792, 9783540366799
Publication statusPublished - 1 Jan 2006
EventInternational Conference on Embedded and Ubiquitous Computing, EUC 2006 - Seoul, Korea, Republic of
Duration: 1 Aug 20064 Aug 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4096 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Embedded and Ubiquitous Computing, EUC 2006
Country/TerritoryKorea, Republic of
CitySeoul
Period1/08/064/08/06

Keywords

  • Clustered BitTorrent (CBT)
  • Peer-to-peer networks
  • Proximity-aware
  • Scalability
  • Super-peer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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