Simulation of nonGaussian Long-Range-Dependent using Wavelets Department of Electrical and Computer Engineering Rice University 6100 South Main Street Houston, TX 77005, USA Traffic Vinay J. Ribeiro, Rudolf H. Riedi, Matthew S. Crouse, and Richard G. Baraniuk * Abstract In this paper, we develop a simple and powerful multiscale model for the synthesis of nonGaussian, 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 multiplicative 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. 1 Introduction LRD of data traffic can
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Simulation of nonGaussian long-range-dependent traffic using wavelets
Ribeiro, Vinay J.; Riedi, Rudolf H.; Crouse, Matthew S.; Baraniuk, Richard G.
Follow ACM SIGMETRICS Performance Evaluation Review
, Volume 27 (1)
Association for Computing Machinery – Jun 1, 1999
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