Distributed proximity-aware peer clustering in BitTorrent-like peer-to-peer networks

Bin Xiao; J.D. Yu; Zili Shao; M.L. Li

doi:10.1007/11802167_39

Distributed proximity-aware peer clustering in BitTorrent-like peer-to-peer networks

Bin Xiao, J.D. Yu, Zili Shao, M.L. Li

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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 language	English
Title of host publication	Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)
Publisher	Springer
Pages	375-384
Number of pages	10
ISBN (Electronic)	9783540366812
ISBN (Print)	9783540366799
DOIs	https://doi.org/10.1007/11802167_39
Publication status	Published - 2006
Event	International Conference on Embedded and Ubiquitous Computing [EUC] - Duration: 1 Jan 2006 → …

Conference

Conference	International Conference on Embedded and Ubiquitous Computing [EUC]
Period	1/01/06 → …

Keywords

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

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

More information

10.1007/11802167_39

Cite this

@inproceedings{73a119b98aad458ab1a812f716459342,

title = "Distributed proximity-aware peer clustering in BitTorrent-like peer-to-peer networks",

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.",

keywords = "Proximity-aware, Clustered BitTorrent (CBT), Peer-to-peer networks, Super-peerscalability",

author = "Bin Xiao and J.D. Yu and Zili Shao and M.L. Li",

year = "2006",

doi = "10.1007/11802167_39",

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pages = "375--384",

booktitle = "Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics)",

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Xiao, B, Yu, JD, Shao, Z & Li, ML 2006, Distributed proximity-aware peer clustering in BitTorrent-like peer-to-peer networks. in Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, pp. 375-384, International Conference on Embedded and Ubiquitous Computing [EUC], 1/01/06. https://doi.org/10.1007/11802167_39

Distributed proximity-aware peer clustering in BitTorrent-like peer-to-peer networks. / Xiao, Bin; Yu, J.D.; Shao, Zili et al.
Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, 2006. p. 375-384.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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N2 - 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.

AB - 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.

KW - Proximity-aware

KW - Clustered BitTorrent (CBT)

KW - Peer-to-peer networks

KW - Super-peerscalability

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M3 - Conference article published in proceeding or book

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ER -

Distributed proximity-aware peer clustering in BitTorrent-like peer-to-peer networks

Abstract

Conference

Keywords

ASJC Scopus subject areas

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