Simulation of Non-Gaussian Long-Range-Dependent Traffic using Wavelets

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Title: Simulation of Non-Gaussian Long-Range-Dependent Traffic using Wavelets
Author: Ribeiro, Vinay Joseph; Riedi, Rudolf H.; Crouse, Matthew; Baraniuk, Richard G.
Type: Conference paper
xmlui.Rice_ECE.Keywords: long-range dependent (LRD) network traffic; non-Gaussian; wavelets; Haar transform
Citation: V. J. Ribeiro, R. H. Riedi, M. Crouse and R. G. Baraniuk, "Simulation of Non-Gaussian Long-Range-Dependent Traffic using Wavelets," 1999.
Abstract: In this paper, we develop a simple and powerful multiscale model for the synthesis of non-Gaussian, long-range dependent (LRD) network traffic. Although wavelets effectively decorrelate LRD data, wavelet-based models have generally been restricted by a Gaussianity assumption that can be unrealistic for traffic. Using a ultiplicative superstructure on top of the Haar wavelet transform, we exploit the decorrelating properties of wavelets while simultaneously capturing the positivity and "spikiness" of nonGaussian traffic. This leads to a swift O(N) algorithm for fitting and synthesizing N-point data sets. The resulting model belongs to the class of multifractal cascades, a set of processes with rich statistical properties. We elucidate our model's ability to capture the covariance structure of real data and then fit it to real traffic traces. Queueing experiments demonstrate the accuracy of the model for matching real data. Our results indicate that the nonGaussian nature of traffic has a significant effect on queuing.
Date Published: 1999-05-01

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  • ECE Publications [1054 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.