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).
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Telek, M., Bobbio, A., Jereb, L., Puliafito, A., Trivedi, K.S. (1995). Steady state analysis of Markov Regenerative SPN with age memory policy. In: Beilner, H., Bause, F. (eds) Quantitative Evaluation of Computing and Communication Systems. TOOLS 1995. Lecture Notes in Computer Science, vol 977. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0024314
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DOI: https://doi.org/10.1007/BFb0024314
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