A genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks

Author

Koo, Simon G M ; Lee, C. S George ; Kannan, Karthik

Author_Institution

Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN

fYear

2004

fDate

11-13 Oct. 2004

Firstpage

469

Lastpage

474

Abstract

BitTorrent is a popular, open-source, hybrid peer-to-peer content distribution system that is conducive for distribution of large-volume contents. In this paper, we propose a genetic-algorithm-based neighbor-selection strategy for hybrid peer-to-peer networks, which enhances the decision process performed at the tracker for transfer coordination. We also investigate how the strategy affects system throughput and distribution efficiency as well as peer contributions. We show through computer simulations that by increasing content availability to the clients from their immediate neighbors, it can significantly improve the system performance without trading off users´ satisfaction. The proposed strategy can significantly improve the efficiency of distribution, especially for low-connectivity peers, and it is suitable to deploy for online decisions

Keywords

genetic algorithms; peer-to-peer computing; telecommunication networks; BitTorrent; content distribution system; genetic-algorithm; hybrid peer-to-peer network; neighbor-selection strategy; Availability; Computer network management; Computer networks; Content management; Distributed computing; Engineering management; Intelligent networks; Linux; Peer to peer computing; System performance;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Communications and Networks, 2004. ICCCN 2004. Proceedings. 13th International Conference on

Conference_Location

Chicago, IL

ISSN

1095-2055

Print_ISBN

0-7803-8814-3

Type

conf

DOI

10.1109/ICCCN.2004.1401710

Filename

1401710