Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameIEEE INFOCOMen_US
dc.citation.firstpage1026en_US
dc.citation.lastpage1035en_US
dc.citation.locationTel Aviv, Israelen_US
dc.citation.volumeNumber2en_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.accessioned2007-10-31T01:00:11Zen_US
dc.date.available2007-10-31T01:00:11Zen_US
dc.date.issued2000-03-01en_US
dc.date.modified2006-06-21en_US
dc.date.note2001-08-19en_US
dc.date.submitted2000-03-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractMany 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 <i>O</i>(<i>N</i>)computations. Leveraging the tree structure of the model, we derive a <i>multiscale queuing analysis</i> 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.en_US
dc.description.sponsorshipDefense Advanced Research Projects Agencyen_US
dc.description.sponsorshipOffice of Naval Researchen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.identifier.citationV. J. Ribeiro, R. H. Riedi, M. Crouse and R. G. Baraniuk, "Multiscale Queuing Analysis of Long-Range-Dependent Network Traffic," vol. 2, 2000.en_US
dc.identifier.doihttp://dx.doi.org/10.1109/INFCOM.2000.832278en_US
dc.identifier.urihttps://hdl.handle.net/1911/20247en_US
dc.language.isoengen_US
dc.subjectlong-range dependence (LRD)en_US
dc.subjectmultifractal wavelet model (MWM)en_US
dc.subjectmultiscale queuing analysisen_US
dc.subjectGaussianen_US
dc.subject.keywordlong-range dependence (LRD)en_US
dc.subject.keywordmultifractal wavelet model (MWM)en_US
dc.subject.keywordmultiscale queuing analysisen_US
dc.subject.keywordGaussianen_US
dc.titleMultiscale Queuing Analysis of Long-Range-Dependent Network Trafficen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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