Additive and multiplicative mixture trees for network traffic modeling

Shriram Sarvotham, Xin Wang, Rudolf H. Riedi, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Network traffic exhibits drastically different statistics, ranging from nearly Gaussian marginals and Long range dependence at very large time scales to highly non-Gaussian marginals and multifractal scaling on small scales. This behavior can be explained by forming two components of the traffic according to the speed of connections, one component absorbing most traffic and being mostly Gaussian, the other constituting virtually all the small scale bursts. Towards a better understanding of this phenomenon, we propose a novel tree-based model which is flexible enough to accommodate Gaussian as well as bursty behavior on different scales in a parsimonious way.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
StatePublished - 2002
Event2002 IEEE International Conference on Acoustic, Speech, and Signal Processing - Orlando, FL, United States
Duration: May 13 2002May 17 2002

Other

Other2002 IEEE International Conference on Acoustic, Speech, and Signal Processing
CountryUnited States
CityOrlando, FL
Period5/13/025/17/02

Keywords

  • Fractional Brownian motion
  • Haar wavelet
  • Lévy stable motion
  • Long range dependence
  • Multifractals
  • Network traffic modeling

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Signal Processing
  • Acoustics and Ultrasonics

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