Technical Report Details
|Massimiliano De Pierro|
|Scientific Area:||Performance Evaluation|
|Title:||Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets without Unfolding|
|Publisher:||DiSIT, Computer Science Institute, UPO|
|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)|