Analyzing measurements from data with underlying dependences and heavy-tailed distributions

Natalia M. Markovich, Udo R. Krieger

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We consider measurements that are arising from a next generation network and present advanced mathematical techniques to cope with the analysis and modeling of the gathered data. These statistical techniques are required to study important performance indices of new real-time services in a multimedia Internet such as the demanded bandwidth or delay-loss profiles of packet flows during a session. The latter data sets incorporate strongly correlated or long-range dependent time series and heavy-tailed marginal distributions determining the underlying random variables of the data features. To illustrate the proposed statistical analysis concept, we use traces arising from the popular peer-to-peer video streaming application SopCast.

Original languageEnglish
Title of host publicationICPE'11 - Proceedings of the 2nd Joint WOSP/SIPEW International Conference on Performance Engineering
Pages425-436
Number of pages12
DOIs
StatePublished - 18 Apr 2011
Externally publishedYes
Event2nd Joint WOSP/SIPEW International Conference on Performance Engineering, ICPE 2011 - Karlsruhe, Germany
Duration: 14 Mar 201116 Mar 2011

Publication series

NameICPE'11 - Proceedings of the 2nd Joint WOSP/SIPEW International Conference on Performance Engineering

Conference

Conference2nd Joint WOSP/SIPEW International Conference on Performance Engineering, ICPE 2011
Country/TerritoryGermany
CityKarlsruhe
Period14/03/1116/03/11

Keywords

  • Data analysis
  • Heavy-tailed distributions
  • Long-range dependence
  • NGN traffic characterization
  • Peer-to-peer packet traffic

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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