Generalized and Deterministic Stochastic Petri Nets (GSPN and DSPN) and their relative solvers have been around for a while. In the last years research on GSPN solution has mainly concentrated on efficient data structure for state space representation, while little has been published on DSPN. Strangely enough although, the DSPN solvers available do still exhibit a number of limitations, most notably that no steady state solver is available for non-ergodic DSPNs. This demo tool presentation introduces the new DSPN solver that was developed, and which has been inserted in GreatSPN. With the occasion also a new GSPN solver has been added, which allows for more flexibility in the net definition, and an easier integration with other tools.

DSPN-Tool: A New DSPN and GSPN Solver for GreatSPN

AMPARORE, ELVIO GILBERTO;DONATELLI, Susanna
2010-01-01

Abstract

Generalized and Deterministic Stochastic Petri Nets (GSPN and DSPN) and their relative solvers have been around for a while. In the last years research on GSPN solution has mainly concentrated on efficient data structure for state space representation, while little has been published on DSPN. Strangely enough although, the DSPN solvers available do still exhibit a number of limitations, most notably that no steady state solver is available for non-ergodic DSPNs. This demo tool presentation introduces the new DSPN solver that was developed, and which has been inserted in GreatSPN. With the occasion also a new GSPN solver has been added, which allows for more flexibility in the net definition, and an easier integration with other tools.
2010
QEST 2010, Seventh International Conference on the Quantitative Evaluation of Systems
Williamsburg, Viginia, USA
15-18 September 2010
Proceedings of QEST 2010, Seventh International Conference on the Quantitative Evaluation of Systems
IEEE Computer Society
79
80
9780769541884
http://dx.doi.org/10.1109/QEST.2010.17
Elvio Gilberto Amparore; Susanna Donatelli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/85978
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