Wavelets and multifractals for network traffic modeling and inference

V. J. Ribeiro, R. H. Riedi, R. G. Baraniuk

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

16 Scopus citations

Abstract

This paper reviews the multifractal wavelet model (MWM) and its applications to network traffic modeling and inference. The discovery of the fractal nature of traffic has made new models and analysis tools for traffic essential, since classical Poisson and Markov models do not capture important fractal properties like multiscale variability and burstiness that deleteriously affect performance. Set in the framework of multiplicative cascades, the MWM provides a link to multifractal analysis, a natural tool to characterize burstiness. The simple structure of the MWM enables fast O(N) synthesis of traffic for simulations and a tractable queuing analysis, thus rendering it suitable for real networking applications including end-to-end path modeling.

Original languageEnglish (US)
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages3429-3432
Number of pages4
Volume6
StatePublished - 2001
Event2001 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing - Salt Lake, UT, United States
Duration: May 7 2001May 11 2001

Other

Other2001 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing
CountryUnited States
CitySalt Lake, UT
Period5/7/015/11/01

ASJC Scopus subject areas

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

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