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giovedì, 13 dicembre 2018

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Autori:Marco Beccuti
Lorenzo Capra
Massimiliano De Pierro
Giuliana Franceschinis
Simone Pernice
Area Scientifica:Performance Evaluation
Titolo:Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets without Unfolding
Apparso su:TR-INF-2018-07-03-UNIPMN
Editore:DiSIT, Computer Science Institute, UPO
Anno:2018
Tipo Pubblicazione:Technical Report
URL:http://www.di.unipmn.it...R-INF-2018-07-03-UNIPMN.pdf
Sommario:This report concerns the quantitative evaluation of Stochastic Symmetric Nets (SSN) by means of a fluid approximation technique particularly suited to analysing systems with huge state space. In particular a new efficient approach is proposed to derive the deterministic process approximating the original stochastic process through a system of Ordinary Differential Equations (ODE). The intrinsic symmetry of SSN models is exploited to significantly reduce the size of the ODE system while a symbolic calculus operating on the SSN arc functions is employed to derive such system efficiently, avoiding the complete unfolding of the SSN model into a Stochastic Petri Net (SPN)