Toward an Improved Understanding of Network Traffic Dynamics

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dc.citation.bibtexNameinbooken_US
dc.citation.firstpage507en_US
dc.citation.journalTitleSelf-similar Network Traffic and Performance Evaluation, eds Park and Willinger, Wileyen_US
dc.citation.lastpage530en_US
dc.contributor.authorRiedi, Rudolf H.en_US
dc.contributor.authorWillinger, Walteren_US
dc.contributor.editorPark and Willingeren_US
dc.contributor.orgCenter for Multimedia Communications (http://cmc.rice.edu/)en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T01:01:34Zen_US
dc.date.available2007-10-31T01:01:34Zen_US
dc.date.issued2000-01-15en_US
dc.date.modified2002-12-05en_US
dc.date.submitted2002-12-05en_US
dc.descriptionBook chapteren_US
dc.description.abstractSince the discovery of long range dependence in Ethernet LAN traces there has been significant progress in developing appropriate mathematical and statistical techniques that provide a physical-based, networking-related understanding of the observed fractal-like or self-similar scaling behavior of measured data traffic over time scales ranging from hundreds of milliseconds to seconds and beyond. These developments have helped immensely in demystifying fractal-based traffic modeling and have given rise to new insights and physical understanding of the effects of large-time scaling properties in measured network traffic on the design, management and performance of high-speed networks. However, to provide a complete description of data network traffic, the same kind of understanding is necessary with respect to the dynamic nature of traffic over small time scales, from a few hundreds of milliseconds downwards. Because of the predominant protocols and end-to-end congestion control mechanisms that determine the flow of packets, studying the fine-time scale behavior or local characteristics of data traffic is intimately related to understanding the complex interactions that exist in data networks. In this chapter, we first summarize the results that provide a unifying and consistent picture of the large-time scaling behavior of data traffic. We then report on recent progress in studying the small-time scaling behavior in data network traffic and outline a number of challenging open problems that stand in the way of providing an understanding of the local traffic characteristics that is as plausible, intuitive, appealing and relevant as the one that has been found for the global or large-time scaling properties of data traffic.en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.identifier.citationR. H. Riedi and W. Willinger, "Toward an Improved Understanding of Network Traffic Dynamics," <i>Self-similar Network Traffic and Performance Evaluation, eds Park and Willinger, Wiley,</i> 2000.en_US
dc.identifier.urihttps://hdl.handle.net/1911/20276en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.subjectSelf-similaren_US
dc.subjectmultifractalen_US
dc.subjectnetwork trafficen_US
dc.subject.keywordSelf-similaren_US
dc.subject.keywordmultifractalen_US
dc.subject.keywordnetwork trafficen_US
dc.subject.otherWavelet based Signal/Image Processingen_US
dc.subject.otherMultiscale Methodsen_US
dc.subject.otherSignal Processing for Networkingen_US
dc.subject.otherMultifractalsen_US
dc.titleToward an Improved Understanding of Network Traffic Dynamicsen_US
dc.typeBook chapteren_US
dc.type.dcmiTexten_US
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