Markov Regenerative Stochastic Petri Nets to Model and Evaluate Phased Mission Systems Dependability
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In this paper we focus on the analytical modeling for the dependability evaluation of phased-mission systems. Because of their dynamic behavior, systems showing a phased behavior offer challenges in modeling. We propose the modeling and evaluation of phased-mission systems dependability through the Deterministic and Stochastic Petri Nets (DSPN). The DSPN approach to the phased-mission systems offers many advantages, concerning both the modeling and the solution. The DSPN model of the mission can be a very concise one, and it can be efficiently solved for the dependability evaluation purposes. The solution procedure is supported by the existence of an analytical solution for the transient probabilities of the marking process underlying the DSPN model. This analytical solution can be fully automated. We show how the DSPN models capabilities are able to deal with various peculiar features of phased-mission systems, including those systems where the next phase to be performed can be chosen at the time the preceding phase ends.