Deriving Symbolic Ordinary Differential Equations From Stochastic Symmetric Nets Without Unfolding
COMPUTER PERFORMANCE ENGINEERING (EPEW 2018)(2018)
摘要
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).
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关键词
Stochastic Symmetric Nets, Ordinary Differential Equations, Symmetries, Symbolic analysis, Symbolic structural techniques
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