Journal article
A Markovian approach for modeling packet traffic with long range dependence
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
| Language: | English |
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| Publisher: | IEEE |
| Year: | 1998 |
| Pages: | 719-732 |
| ISSN: | 07338716 and 15580008 |
| Types: | Journal article |
| DOI: | 10.1109/49.700908 |
| ORCIDs: | Nielsen, Bo Friis |
Area measurement Bit rate Context modeling Displays Local area networks Markov modulated Poisson processes Markov processes System performance Telecommunication traffic Telephony Time measurement Traffic control correlation methods counting process fitting procedure four two-state MMPP interarrival time correlations long-range dependence modulation packet switching packet traffic modelling second-order properties second-order self-similar process stochastic processes superpositions telecommunication traffic time scales two-state MMPP
