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<p><b>Abstract</b>—Petri nets represent a useful tool for performance, dependability, and performability analysis of complex systems. Their modeling power can be increased even more if nonexponentially distributed events are considered. However, the inclusion of nonexponential distributions destroys the memoryless property and requires to specify how the marking process is conditioned upon its past history. In this paper, we consider, in particular, the class of stochastic Petri nets whose marking process can be mapped into a Markov regenerative process. An adequate mathematical framework is developed to deal with the considered class of Markov Regenerative Stochastic Petri Nets (<it>MRSPN</it>). An unified approach for the solution of <it>MRSPN</it>s where different preemption policies can be defined in the same model is presented. The solution is provided both in steady-state and in transient condition. An example concludes the paper.</p>
Stochastic Petri Nets, Markov regenerative processes, preemptive policies, transient and steady-state analysis.

A. Bobbio, M. Telek and A. Puliafito, "A Modeling Framework to Implement Preemption Policies in Non-Markovian SPNs," in IEEE Transactions on Software Engineering, vol. 26, no. , pp. 36-54, 2000.
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