Generalized stochastic Petri nets (GSPN), with immediate transitions, are extensively used to model concurrent systems in a wide range of application domains, particularly including software and hardware aspects of computer systems, and their interactions. These models are typically used for system specification, logical and performance analysis, or automatic code generation. In order to keep modeling separate from the analysis and to gain in efficiency and robustness of the modeling process, the complete specification of the stochastic process underlying a model should be guaranteed at the net level, without requiring the generation and exploration of the state space. In this paper, we propose a net-level method that guides the modeler in the task of defining the priorities (and weights) of immediate transitions in a GSPN model, to deal with confusion and conflict problems. The application of this method ensures well-definition without reducing modeling flexibility or expressiveness.

Well-Defined Generalized Stochastic Petri Nets: A Net-Level Method to Specify Priorities

FRANCESCHINIS, Giuliana Annamaria;
2003-01-01

Abstract

Generalized stochastic Petri nets (GSPN), with immediate transitions, are extensively used to model concurrent systems in a wide range of application domains, particularly including software and hardware aspects of computer systems, and their interactions. These models are typically used for system specification, logical and performance analysis, or automatic code generation. In order to keep modeling separate from the analysis and to gain in efficiency and robustness of the modeling process, the complete specification of the stochastic process underlying a model should be guaranteed at the net level, without requiring the generation and exploration of the state space. In this paper, we propose a net-level method that guides the modeler in the task of defining the priorities (and weights) of immediate transitions in a GSPN model, to deal with confusion and conflict problems. The application of this method ensures well-definition without reducing modeling flexibility or expressiveness.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11579/14067
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 20
  • ???jsp.display-item.citation.isi??? 13
social impact