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dc.contributor.authorRibeiro, Vinay Joseph
Riedi, Rudolf H.
Crouse, Matthew
Baraniuk, Richard G.
dc.creatorRibeiro, Vinay Joseph
Riedi, Rudolf H.
Crouse, Matthew
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T01:00:11Z
dc.date.available 2007-10-31T01:00:11Z
dc.date.issued 2000-03-01
dc.date.submitted 2000-03-01
dc.identifier.citation V. J. Ribeiro, R. H. Riedi, M. Crouse and R. G. Baraniuk, "Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic," vol. 2, 2000.
dc.identifier.urihttps://hdl.handle.net/1911/20247
dc.description Conference Paper
dc.description.abstract Many studies have indicated the importance of capturing scaling properties when modeling traffic loads; however, the influence of long-range dependence (LRD) and marginal statistics still remains on unsure footing. In this paper, we study these two issues by introducing a multiscale traffic model and a novel multiscale approach to queuing analysis. The multifractal wavelet model (MWM) is a multiplicative, wavelet-based model that captures the positivity, LRD, and "spikiness" of non-Gaussian traffic. Using a binary tree, the model synthesizes an N-point data set with only O(N)computations. Leveraging the tree structure of the model, we derive a multiscale queuing analysis that provides a simple closed form approximation to the tail queue probability, valid for any given buffer size. The analysis is applicable not only to the MWM but to tree-based models in general, including fractional Gaussian noise. Simulated queuing experiments demonstrate the accuracy of the MWM for matching real data traces and the precision of our theoretical queuing formula. Thus, the MWM is useful not only for fast synthesis of data for simulation purposes but also for applications requiring accurate queuing formulas such as call admission control. Our results clearly indicate that the marginal distribution of traffic at different time-resolutions affects queuing and that a Gaussian assumption can lead to over-optimistic predictions of tail queue probability even when taking LRD into account.
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship Office of Naval Research
dc.description.sponsorship National Science Foundation
dc.language.iso eng
dc.subjectlong-range dependence (LRD)
multifractal wavelet model (MWM)
multiscale queuing analysis
Gaussian
dc.title Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic
dc.type Conference paper
dc.date.note 2001-08-19
dc.citation.bibtexName inproceedings
dc.date.modified 2006-06-21
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)
dc.subject.keywordlong-range dependence (LRD)
multifractal wavelet model (MWM)
multiscale queuing analysis
Gaussian
dc.citation.volumeNumber 2
dc.citation.location Tel Aviv, Israel
dc.citation.conferenceName IEEE INFOCOM
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1109/INFCOM.2000.832278
dc.citation.firstpage 1026
dc.citation.lastpage 1035


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1450]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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