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EnglishThis 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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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| PaperSymbODE.pdf | 1 Ver. finale autore | Utenti riconosciuti Richiedi una copia |
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