Stochastic Petri Nets Sensitivity to Token Scheduling Policies

Balbo, Gianfranco; Beccuti, Marco; Pierro, Massimiliano De; Franceschinis, Giuliana

doi:10.1007/978-3-642-20009-0_29

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…As it happens in the fork and join example, it is possible in general to observe that at any instant (at any system state) a place invariant is satisfied by the colored tokens representing a customer γi. Such invariant states a conservation of the tokens representing the customers 3 .…”

Section: Modeling Customers In Swnmentioning

confidence: 99%

“…In [4] the authors show that in GSPN models the customers queueing policy may influence the passage time distribution, while it is irrelevant for the computation of average performance indices (due to the memoryless property of the exponential distribution and to the fact that tokens in GSPNs cannot be distinguished). In the context of the SWN formalism, since tokens can be distinguished through colors, the queueing policy may influence also the average performance indices 5 [3]. Although the SWN is powerful enough to directly model any queueing policy, the addition of some "syntactic sugar" may help the modeler in correctly specifying the order of extraction of customers from places as well as various service specifications.…”

Section: Some Subtle Modeling Issuesmentioning

confidence: 99%

See 1 more Smart Citation

Computing first passage time distributions in stochastic well-formed nets

Balbo

Beccuti

Pierro

et al. 2011

Proceedings of the 2nd ACM/SPEC International Conference on Performance Engineering

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The increasing demand for customer centric evaluation of systems, mostly related with the assessment of the quality of service that they can deliver, requires the development of techniques properly designed to model and to study the movement of specific entities generically referred to as "customers". Stochastic Well-Formed Net (SWN) are naturally suited for the representation of systems in which "customers" of different categories compete for the use of common resources. Color classes of SWN are easily associated with these different categories, leaving to the peculiar features of the formalism the possibility of exploiting all the symmetries existing into the representation for the efficient and effective computation of the measures of interest. Within this application context, the computation of first passage time distribution measures in SWN is becoming of primary interest. Customers however are not primitive entities in the formalism and an approach similar to that previously developed for Generalized Stochastic Petri Nets (GSPN) is suggested to overcome this problem in which P-semiflows are used to identify the customers. In this paper we propose an original algorithm for computing some P-semiflows of colored PNs in parametric form by exploiting the peculiarities of the objective of this investigation, and extend the customer centric first passage time computation approach previously developed for GSPNs, to make it suitable for SWN models. Moreover, the paper proposes an enhancement of the SWN notation in order to provide a way to ease the modeler in the specification of customer scheduling policies that may affect the computation of first passage time distributions. This extension, inspired by Queueing Petri Nets, adds to SWN some "syntactic sugar" that allows to include in the model queueing places which are automatically replaced by appropriate submodels, before solving the model.

show abstract

Section: Modeling Customers In Swnmentioning

confidence: 99%

Section: Some Subtle Modeling Issuesmentioning

confidence: 99%

Computing first passage time distributions in stochastic well-formed nets

Balbo

Beccuti

Pierro

et al. 2011

Proceedings of the 2nd ACM/SPEC International Conference on Performance Engineering

View full text Add to dashboard Cite

show abstract

“…In the context of the SWN formalism, since tokens can be distinguished through colors, the queueing policy may influence also the average performance indices 5 [3]. Although the SWN is powerful enough to directly model any queueing policy, the addition of some "syntactic sugar" may help the modeler in correctly specifying the order of extraction of customers from places as well as various service specifications.…”

Section: Some Subtle Modeling Issuesmentioning

confidence: 99%

“…Furthermore, in our approach SWNs are extended with some "syntactic sugar" in order to provide a way to ease the modeler in the specification of customer queueing policies which, as pointed out in literature [3], may affect the computation of the first passage time distribution in a substantial manner. This aspect is illustrated through some experiments.…”

Section: Introductionmentioning

confidence: 99%