Steady state analysis of Markov Regenerative SPN with age memory policy View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1995

AUTHORS

Miklós Telek , Andrea Bobbio , László Jereb , Antonio Puliafito , Kishor S. Trivedi

ABSTRACT

Non-Markovian Stochastic Petri Nets (SPN) have been developed as a tool to deal with systems characterized by non exponentially distributed timed events. Recently, some effort has been devoted to the study of SPN with generally distributed firing times, whose underlying marking process belongs to the class of Markov Regenerative Processes (MRGP). We refer to this class of models as Markov Regenerative SPN (MRSPN). In this paper, we describe a computationally effective algorithm for the steady state solution of MRSPN with age memory policy and subordinated Continuous Time Markov Chain (CTMC). More... »

PAGES

165-179

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0024314

DOI

http://dx.doi.org/10.1007/bfb0024314

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1041792691


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