ACM Transactions on Computer Systems (TOCS)
Regenerative stochastic Petri nets
Performance Evaluation
Petri Net Theory and the Modeling of Systems
Petri Net Theory and the Modeling of Systems
Stochastic Petri Nets with Simultaneous Transition Firings
PNPM '87 The Proceedings of the Second International Workshop on Petri Nets and Performance Models
Extended Stochastic Petri Nets: Applications and Analysis
Performance '84 Proceedings of the Tenth International Symposium on Computer Performance Modelling, Measurement and Evaluation
On the role of generalized semi-Markov processes in simulation output analysis
WSC '83 Proceedings of the 15th conference on Winter simulation - Volume 1
On the integration of delay and throughput measures in distributed processing models
On the integration of delay and throughput measures in distributed processing models
Regenerative simulation methods for local area computer networks
IBM Journal of Research and Development
Modeling of Hierarchical Distributed Systems with Fault-Tolerance
IEEE Transactions on Software Engineering
Petri-net evaluation using APL2
APL '92 Proceedings of the international conference on APL
A Characterization of the Stochastic Process Underlying a Stochastic Petri Net
IEEE Transactions on Software Engineering
Simulation of Task Graph Systems in Heterogeneous Computing Environments
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Transient analysis of non-Markovian models using stochastic state classes
Performance Evaluation
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In the context of discrete event simulation, the marking of a stochastic Petri net (SPN) corresponds to the state of the underlying stochastic process of the simulation and the firing of a transition corresponds to the occurrence of an event. A study is made of the modeling power of SPNs with timed and immediate transitions, showing that such Petri nets provide a general framework for simulation. The principle result is that for any (finite or) countable state GSMP (generalized semi-Markov process) there exists an SPN having a marking process that mimics the GSMP in the sense that the two processes (and their underlying general state-space Markov chains) have the same finite dimensional distributions.