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    Network Traffic Modeling using a Multifractal Wavelet Model

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    Author
    Riedi, Rudolf H.; Ribeiro, Vinay Joseph; Crouse, Matthew; Baraniuk, Richard G.
    Date
    2006-07-19
    Abstract
    In this paper, we develop a simple and powerful multiscale model for syntheizing nonFaussian, 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 un-realistic 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.
    Description
    Conference Paper
    Citation
    R. H. Riedi, V. J. Ribeiro, M. Crouse and R. G. Baraniuk, "Network Traffic Modeling using a Multifractal Wavelet Model," 2000.
    Published Version
    http://dx.doi.org/10.1007/978-3-0348-8266-8_54
    Keyword
    long range dependent network traffic; DSP for Communications; long range dependent network traffic
    Type
    Conference paper
    Citable link to this page
    https://hdl.handle.net/1911/20275
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    • DSP Publications [508]
    • ECE Publications [1468]

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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map