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Analysis of Packet Transmission Processes in Peer-to-Peer Networks by Statistical Inference Methods

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Data Traffic Monitoring and Analysis

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 7754))

Abstract

Applying advanced statistical techniques, we characterize the peculiarities of a locally observed peer population in a popular P2P overlay network. The latter is derived from a mesh-pull architecture. Using flow data collected at a single peer, we show how Pareto and Generalized Pareto models can be applied to classify the local behavior of the population feeding a peer. Our approach is illustrated both by file sharing data of a P2P session generated by a mobile BitTorrent client in a WiMAX testbed and by video data streamed to a stationary client in a SopCast session. These techniques can help us to cope with an efficient adaptation of P2P dissemination protocols to mobile environments.

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Author information

Authors and Affiliations

  1. Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia

    Natalia M. Markovich

  2. WIAI, Otto-Friedrich-University, Bamberg, Germany

    Udo R. Krieger

Authors
  1. Natalia M. Markovich
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  2. Udo R. Krieger
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Editor information

Editors and Affiliations

  1. Networking and Security Department, Eurécom, 450 Route des Chappes, 06410, Biot, France

    Ernst Biersack

  2. Department of Information Engineering, University of Pisa, Via Caruso 16, 56122, Pisa, Italy

    Christian Callegari

  3. Facults of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000, Zagreb, Croatia

    Maja Matijasevic

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Markovich, N.M., Krieger, U.R. (2013). Analysis of Packet Transmission Processes in Peer-to-Peer Networks by Statistical Inference Methods. In: Biersack, E., Callegari, C., Matijasevic, M. (eds) Data Traffic Monitoring and Analysis. Lecture Notes in Computer Science, vol 7754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36784-7_5

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  • DOI: https://doi.org/10.1007/978-3-642-36784-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36783-0

  • Online ISBN: 978-3-642-36784-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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