A comparison of Markovian Arrival and ARMA/ARTA processes for the modeling of correlated input processes

The adequate modeling of input processes often requires that correlation is taken into account and is a key issue in building realistic simulation models. In analytical modeling Markovian Arrival Processes (MAPs) are commonly used to describe correlated arrivals, whereas for simulation often ARMA/AR...

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Bibliographic Details
Published inProceedings of the 2009 Winter Simulation Conference (WSC) pp. 634 - 645
Main Authors Bause, F., Buchholz, P., Kriege, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
Subjects
Analytical models
Application software
Computational modeling
Computer networks
Gaussian distribution
Markov processes
Numerical models
Random variables
Solid modeling
Stochastic processes
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Summary:The adequate modeling of input processes often requires that correlation is taken into account and is a key issue in building realistic simulation models. In analytical modeling Markovian Arrival Processes (MAPs) are commonly used to describe correlated arrivals, whereas for simulation often ARMA/ARTA-based models are in use. Determining the parameters for the latter input models is well-known whereas good fitting methods for MAPs have been developed only in recent years. Since MAPs may as well be used in simulation models, it is natural to compare them with ARMA/ARTA models according to their expressiveness and modeling capabilities for dependent sequences. In this paper we experimentally compare MAPs and ARMA/ARTA-based models.
ISBN:9781424457700
142445770X
ISSN:0891-7736
1558-4305
DOI:10.1109/WSC.2009.5429563