deposit_your_work

Wavelets and Multifractals for Network Traffic Modeling and Inference

Files in this item

Files Size Format View
Rib2001May5Waveletsan.PDF 406.9Kb application/pdf Thumbnail
Rib2001May5Waveletsan.PS 366.5Kb application/postscript View/Open

Show full item record

Item Metadata

Title: Wavelets and Multifractals for Network Traffic Modeling and Inference
Author: Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Baraniuk, Richard G.
Type: Conference paper
Keywords: multifractal wavelet model; MWM; Poisson; Markov
Citation: V. J. Ribeiro, R. H. Riedi and R. G. Baraniuk, "Wavelets and Multifractals for Network Traffic Modeling and Inference," vol. 6, pp. 3429-3432, 2001.
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 synthesis of traffic for simulations and a tractable queuing analysis, thus rendering it suitable for real networking applications including end-to-end path modeling.
Date Published: 2001-05-01

This item appears in the following Collection(s)

  • ECE Publications [1048 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.