This paper concerns the quantitative evaluation of Stochastic Symmetric Nets (SSN) by means of a fluid approximation technique particularly suited to analyse systems with a 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).
Titolo: | Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding |
Autori Riconosciuti: | |
Autori: | Marco Beccuti, Lorenzo Capra, Massimiliano De Pierro, Giuliana Franceschinis, Simone Pernice |
Data di pubblicazione: | 2018 |
Abstract: | This paper concerns the quantitative evaluation of Stochastic Symmetric Nets (SSN) by means of a fluid approximation technique particularly suited to analyse systems with a 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). |
Editore: | Springer International Publishing |
Titolo del libro: | Computer Performance Engineering |
Volume: | 11178 |
Pagina iniziale: | 30 |
Pagina finale: | 45 |
Nome del convegno: | European Workshop on Performance Engineering |
Luogo del convegno: | Paris, France |
Anno del convegno: | October 29-30, 2018 |
Digital Object Identifier (DOI): | 10.1007/978-3-030-02227-3_3 |
ISBN: | 978-3-030-02226-6 978-3-030-02227-3 |
URL: | https://link.springer.com/chapter/10.1007%2F978-3-030-02227-3_3 |
Appare nelle tipologie: | 04A-Conference paper in volume |
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