Open Access
Approximation methods for stochastic petri nets
Hauke Joerg Jungnitz
- 01 May 1992
15
TL;DR: A response time approximation technique for the performance analysis is introduced and delay equivalence, which has previously been introduced in the context of marked graphs by Woodside et al., Marie's method and flow equivalent aggregation are applied to the aggregated net systems.
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Abstract: Stochastic Marked Graphs are a concurrent decision free formalism provided with a powerful synchronization mechanism generalizing conventional Fork Join Queueing Networks. In some particular cases the analysis of the throughput can be done analytically. Otherwise the analysis suffers from the classical state explosion problem. Embedded in the divide and conquer paradigm, approximation techniques are introduced for the analysis of stochastic marked graphs and Macroplace/Macrotransition-nets (MPMT-nets), a new subclass introduced herein. MPMT-nets are a subclass of Petri nets that allow limited choice, concurrency and sharing of resources. The modeling power of MPMT is much larger than that of marked graphs, e.g., MPMT-nets can model manufacturing flow lines with unreliable machines and dataflow graphs where choice and synchronization occur. The basic idea leads to the notion of a cut to split the original net system into two subnets. The cuts lead to two aggregated net systems where one of the subnets is reduced to a single transition. A further reduction leads to a basic skeleton. The generalization of the idea leads to multiple cuts, where single cuts can be applied recursively leading to a hierarchical decomposition. Based on the decomposition, a response time approximation technique for the performance analysis is introduced. Also, delay equivalence, which has previously been introduced in the context of marked graphs by Woodside et al., Marie's method and flow equivalent aggregation are applied to the aggregated net systems. The experimental results show that response time approximation converges quickly and shows reasonable accuracy in most cases. The convergence of Marie's method and flow equivalent aggregation are applied to the aggregated net systems. The experimental results show that response time approximation converges quickly and shows reasonable accuracy in most cases. The convergence of Marie's is slower, but the accuracy is generally better. Delay equivalence often fails to converge, while flow equivalent aggregation can lead to potentially bad results if a strong dependence of the mean completion time on the interarrival process exists.
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Citations
Fundamentals of Queueing Theory
Rodney Coleman
- 01 May 1975
TL;DR: The Fundamentals of Queueing Theory, Fourth Edition as discussed by the authors provides a comprehensive overview of simple and more advanced queuing models, with a self-contained presentation of key concepts and formulae.
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Petri nets as discrete event models for supervisory control
Alessandro Giua
- 01 Jan 1992
TL;DR: An algorithm is presented, based on the concurrent composition operator, for the design of Petri net supervisors and how a composed net may be validated and it is shown that a PetriNet supervisor may not exist if the system's behavior or the legal behavior are nonregular Petri Net languages.
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Approximate throughput computation of stochastic marked graphs
TL;DR: A general iterative technique for approximate throughput computation of stochastic strongly connected marked graphs through a single input-single output cut, allowing the split of the model through any cut.
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Complete decomposition of stochastic Petri nets representing generalized service networks
Yao Li,C.M. Woodside +1 more
TL;DR: The new method adapts a delay equivalence decomposition technique used previously to reduce the solution complexity of the auxiliary models which must be solved in the iteration, applied to a scalable model for computer system performance.
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Exploiting structure in solution: decomposing compositional models
Jane Hillston
- 17 Jan 2002
TL;DR: This chapter considers how far compositionality has been able to take advantage of when it comes to solving the Markov process underlying a Markovian process algebra model.
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