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

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameACM SIGMETRICSen_US
dc.citation.locationAtlanta, GAen_US
dc.contributor.authorRibeiro, Vinay Josephen_US
dc.contributor.authorRiedi, Rudolf H.en_US
dc.contributor.authorCrouse, Matthewen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:00:14Zen_US
dc.date.available2007-10-31T01:00:14Zen_US
dc.date.issued1999-05-01en_US
dc.date.modified2006-06-05en_US
dc.date.note2001-08-16en_US
dc.date.submitted1999-05-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractIn 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.en_US
dc.description.sponsorshipTexas Instrumentsen_US
dc.description.sponsorshipDefense Advanced Research Projects Agencyen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.identifier.citationV. J. Ribeiro, R. H. Riedi, M. Crouse and R. G. Baraniuk, "Simulation of Non-Gaussian Long-Range-Dependent Traffic using Wavelets," 1999.en_US
dc.identifier.doihttp://dx.doi.org/10.1145/301464.301475en_US
dc.identifier.urihttps://hdl.handle.net/1911/20248en_US
dc.language.isoengen_US
dc.subjectlong-range dependent (LRD) network trafficen_US
dc.subjectnon-Gaussianen_US
dc.subjectwaveletsen_US
dc.subjectHaar transformen_US
dc.subject.keywordlong-range dependent (LRD) network trafficen_US
dc.subject.keywordnon-Gaussianen_US
dc.subject.keywordwaveletsen_US
dc.subject.keywordHaar transformen_US
dc.titleSimulation of Non-Gaussian Long-Range-Dependent Traffic using Waveletsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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