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Markov Regenerative Stochastic Petri Nets to Model and Evaluate Phased Mission Systems Dependability
December 2001 (vol. 50 no. 12)
pp. 1337-1351

This study deals with model-based dependability transient analysis of phased mission systems. A review of the studies in the literature showed that several aspects of multiphased systems pose challenging problems to the dependability evaluation methods and tools. To attack the weak points of the state-of-the-art we propose a modeling methodology that exploits the power of the class of Markov regenerative stochastic Petri net models. By exploiting the techniques available in the literature for the analysis of the Markov Regenerative Processes, we obtain an analytical solution technique with a low computational complexity, basically dominated by the cost of the separate analysis of the system inside each phase. Last, the existence of analytical solutions allows us to derive the sensitivity functions of the dependability measures, thus providing the dependability engineer with additional means for the study of phased mission systems.

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Index Terms:
Phased mission systems, analytical modeling and evaluation, Markov regenerative stochastic Petri nets, Markov regenerative processes, dependability, performability, sensitivity analysis
Citation:
I. Mura, A. Bondavalli, "Markov Regenerative Stochastic Petri Nets to Model and Evaluate Phased Mission Systems Dependability," IEEE Transactions on Computers, vol. 50, no. 12, pp. 1337-1351, Dec. 2001, doi:10.1109/TC.2001.970572
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