• DocumentCode
    3143529
  • Title

    Reducing Download Times in Peer-to-Peer File Sharing Systems with Stochastic Service Capacities

  • Author

    Li, Keqin

  • Author_Institution
    Dept. of Comput. Sci., State Univ. of New York, New York, NY, USA
  • fYear
    2011
  • fDate
    16-20 May 2011
  • Firstpage
    608
  • Lastpage
    617
  • Abstract
    The main problem for an individual user peer in a peer-to-peer network with heterogeneous source peers is the peer selection problem, namely, switching among source peers and finally settling on one, while keeping the total time of probing and downloading to a minimum. There has been little investigation on selecting source peers with stochastic service capacities. The main contribution of this paper is to address the problem of reducing download times in peer-to-peer file sharing systems with stochastic service capacities. A precise analysis of the expected download time is given when the service capacity of a source peer is a random variable. A chunk-based switching and peer selection algorithm using the method of probing high-capacity peers is proposed and the expected download time of the algorithm is analyzed. Two sub problems of the optimal choice of the threshold of high-capacity source peers and the optimal order of probing are also solved. The performance of the algorithm is compared with the random chunk-based switching method. It is shown that noticeable performance improvement can be obtained.
  • Keywords
    peer-to-peer computing; stochastic processes; chunk-based switching; download time reduction; peer selection algorithm; peer selection problem; peer-to-peer file sharing system; stochastic service capacity; Algorithm design and analysis; Capacity planning; Peer to peer computing; Probes; Random variables; Stochastic processes; Switches;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on
  • Conference_Location
    Shanghai
  • ISSN
    1530-2075
  • Print_ISBN
    978-1-61284-425-1
  • Electronic_ISBN
    1530-2075
  • Type

    conf

  • DOI
    10.1109/IPDPS.2011.203
  • Filename
    6008883