|Massimiliano De Pierro|
|Area Scientifica:||Performance Evaluation|
|Titolo:||Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets without Unfolding|
|Editore:||DiSIT, Computer Science Institute, UPO|
|Tipo Pubblicazione:||Technical Report|
|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)|