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Paper on Making Full Use of 2-state Markov Processes for Traffic Modeling

posted Jul 29, 2013, 4:03 AM by Patrick Poullie   [ updated Jul 21, 2014, 7:45 AM by Corinna Schmitt ]
The IEEE Infocom 2013 proceedings have just been made available online, including a contribution by DT / TDG partner on the usage of the basic 2-state Markov models for traffic profiles. The presentation explains how to fully exploit 2-state Markov models for adaptation to autocorrelated processes. As the background, an explicite formula is derived for the 2nd order statisitics of 2-state processes, which covers 2-state semi-Markov processes with four transitions specific traffic rate distributions in the most general format.
As the main result, simple 2-state processes are shown to be capable of an amazing good fit of traffic profiles especially with regard to the 2nd order statistics of measured traffic traces, because of a 2-dimensional parameter space is available for adaptation. This means an essential improvement over self-similar and special 2-state models, which are often used but only include a single parameter for fitting the 2nd order statistics, e.g. Markov-modulated Poisson processes (MMPP-2) or Gilbert-Elliot models.