A Modeling Framework to Implement Preemption Policies in Non-Markovian SPNs
IEEE Transactions on Software Engineering
Lectures on formal methods and performance analysis
A Fourth-Order Algorithm with Automatic Stepsize Control for the Transient Analysis of DSPNs
IEEE Transactions on Software Engineering
Discrete-Event Simulation of Fluid Stochastic Petri Nets
IEEE Transactions on Software Engineering
TOOLS '98 Proceedings of the 10th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
Iterative Analysis of Markov Regenerative Models
TOOLS '00 Proceedings of the 11th International Conference on Computer Performance Evaluation: Modelling Techniques and Tools
Discrete-event simulation of fluid stochastic Petri nets
PNPM '97 Proceedings of the 6th International Workshop on Petri Nets and Performance Models
New Primitives for Interlaced Memory Policies in Markov Regenerative Stochastic Petri Nets
PNPM '97 Proceedings of the 6th International Workshop on Petri Nets and Performance Models
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Markov Regenerative Stochastic Petri Nets (MRSPN) have been recently recognized as a valuable tool to model systems with non-exponential timed activities. The usual assumption in the implementation of such models is that at most a single non-exponential transition, with associated enabling memory policy, can be enabled in each marking. More recently, new memory policies have been studied in order to represent more complex and effective preemption mechanisms in real systems. Closed-form solutions in the Laplace transform domain have been provided also in this case. This paper concentrates on the steady-state analysis of MRSPN and provides an unified analytical approach to include mixed memory policies into a single model. A numerical example concludes the paper.