Simulation of nonGaussian long-range-dependent traffic using wavelets

Vinay J. Ribeiro, Rudolf H. Riedi, Matthew S. Crouse, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingChapter

20 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationPerformance Evaluation Review
PublisherACM
Pages1-12
Number of pages12
Volume27
Edition1
StatePublished - Jun 1999
EventProceedings of the 1999 International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS '99 - Atlata, GA, USA
Duration: May 1 1999May 4 1999

Other

OtherProceedings of the 1999 International Conference on Measurement and Modeling of Computer Systems, ACM SIGMETRICS '99
CityAtlata, GA, USA
Period5/1/995/4/99

ASJC Scopus subject areas

  • Hardware and Architecture

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