Markov regenerative stochastic Petri nets
Performance '93 Proceedings of the 16th IFIP Working Group 7.3 international symposium on Computer performance modeling measurement and evaluation
Iterative analysis of Markov regenerative models
Performance Evaluation
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
Performance Analysis of Communication Systems with Non-Markovian Stochastic Petri Nets
Performance Modelling with Deterministic and Stochostic Petri Nets
Performance Modelling with Deterministic and Stochostic Petri Nets
Introduction to Algorithms
On Petri nets with deterministic and exponentially distributed firing times
Advances in Petri Nets 1987, covers the 7th European Workshop on Applications and Theory of Petri Nets
Dependability Modeling and Evaluation of Phased Mission Systems: A DSPN Approach
DCCA '99 Proceedings of the conference on Dependable Computing for Critical Applications
Model Checking Timed and Stochastic Properties with CSL^{TA}
IEEE Transactions on Software Engineering
A component-based solution for reducible Markov regenerative processes
Performance Evaluation
Backward Solution of Markov Chains and Markov Regenerative Processes: Formalization and Applications
Electronic Notes in Theoretical Computer Science (ENTCS)
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This paper presents a new technique for the steady state solution of non-ergodic Markov Regenerative Processes (MRP), based on a structural decomposition of the MRP. Each component may either be a CTMC or a (smaller) MRP. Classical steady state solution methods of MRP are based either on the computation of the embedded Markov chain (EMC) defined over regenerative states, leading to high complexity in time and space (since the EMC is usually dense), or on an iterative scheme that does not require the construction of the EMC. The technique presented is particularly suited for MRPs that exhibit a semi-sequential structure. In this paper we present the new algorithm, its asymptotic complexity, and its performance in comparison with classical MRP techniques. Results are very encouraging, even when the MRP only loosely exhibits the required semi-sequential structure.