A Markovian approach for modeling packet traffic with long range dependence

Publication: Research - peer-reviewJournal article – Annual report year: 1998

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We present a simple Markovian framework for modeling packet traffic with variability over several time scales. We present a fitting procedure for matching second-order properties of counts to that of a second-order self-similar process. Our models essentially consist of superpositions of two-state Markov modulated Poisson processes (MMPPs). We illustrate that a superposition of four two-state MMPPs suffices to model second-order self-similar behavior over several time scales. Our modeling approach allows us to fit to additional descriptors while maintaining the second-order behavior of the counting process. We use this to match interarrival time correlations
Original languageEnglish
JournalSelected Areas in Communications, IEEE Journal on
Publication date1998
Volume16
Journal number5
Pages719-732
ISSN0733-8716
StatePublished
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