Markovian Transition Counting Processes: An Alternative to Markov Modulated Poisson Processes
Abstract
Stochastic models for performance analysis, optimization and control of queues hinge on a multitude of alternatives for input point processes. In case of bursty traffic, one very popular model is the \textit{Markov Modulated Poisson Process} (MMPP), however it is not the only option. Here, we introduce an alternative that we call \textit{Markovian transition counting process} (MTCP). The latter is a point process counting the number of transitions of a finite continuous-time Markov chain. For a given MTCP one can establish an MMPP with the same first and second moments of counts. In this paper, we show the other direction by establishing a duality in terms of first and second moments of counts between MTCPs and a rich class of MMPPs which we refer to as slow MMPPs (modulation is slower than the events). Such a duality confirms the applicability of the MTCP as an alternative to the MMPP which is superior when it comes to moment matching and finding the important measures of the inter-event process. We illustrate the use of such equivalence in a simple queueing example, showing that the MTCP is a comparable and competitive model for performance analysis.
Keywords
Cite
@article{arxiv.2109.00713,
title = {Markovian Transition Counting Processes: An Alternative to Markov Modulated Poisson Processes},
author = {Azam Asanjarani and Sophie Hautphenne and Yoni Nazarathy},
journal= {arXiv preprint arXiv:2109.00713},
year = {2021}
}