Pubblication Details
| Authors: | Marco Beccuti |
| Lorenzo Capra |
| Massimiliano De Pierro |
| Giuliana Franceschinis |
| Simone Pernice |
| Scientific Area: | Performance Evaluation |
| Title: | Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets without Unfolding |
| Published on: | TR-INF-2018-07-03-UNIPMN |
| Publisher: | DiSIT, Computer Science Institute, UPO |
| Year: | 2018 |
| Tipo Pubblicazione: | Technical Report |
| URL: | http://www.di.unipmn.it...R-INF-2018-07-03-UNIPMN.pdf |
| Abstract: | 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) |
