Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods

Natalia M. Markovich; Udo R. Krieger

doi:10.1007/978-3-642-36784-7_5

Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods

Natalia M. Markovich, Udo R. Krieger

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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.

Original language	English
Title of host publication	DataTraffic Monitoring and Analysis
Subtitle of host publication	From Measurement, Classification, and Anomaly Detection to Quality of Experience
Publisher	Springer Verlag
Pages	104-119
Number of pages	16
ISBN (Print)	9783642367830
DOIs	https://doi.org/10.1007/978-3-642-36784-7_5
State	Published - 1 Jan 2013
Externally published	Yes

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7754
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Keywords

Generalized Pareto distribution
change-point detection
heavy hitter model
peer-to-peer network

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-36784-7_5

Cite this

Markovich, N. M., & Krieger, U. R. (2013). Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods. In DataTraffic Monitoring and Analysis: From Measurement, Classification, and Anomaly Detection to Quality of Experience (pp. 104-119). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7754). Springer Verlag. https://doi.org/10.1007/978-3-642-36784-7_5

Markovich, Natalia M. ; Krieger, Udo R. / Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods. DataTraffic Monitoring and Analysis: From Measurement, Classification, and Anomaly Detection to Quality of Experience. Springer Verlag, 2013. pp. 104-119 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Markovich, NM & Krieger, UR 2013, Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods. in DataTraffic Monitoring and Analysis: From Measurement, Classification, and Anomaly Detection to Quality of Experience. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7754, Springer Verlag, pp. 104-119. https://doi.org/10.1007/978-3-642-36784-7_5

Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods. / Markovich, Natalia M.; Krieger, Udo R.
DataTraffic Monitoring and Analysis: From Measurement, Classification, and Anomaly Detection to Quality of Experience. Springer Verlag, 2013. p. 104-119 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7754).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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Markovich NM, Krieger UR. Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods. In DataTraffic Monitoring and Analysis: From Measurement, Classification, and Anomaly Detection to Quality of Experience. Springer Verlag. 2013. p. 104-119. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-36784-7_5

Analysis of packet transmission processes in peer-to-peer networks by statistical inference methods

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