Efficient simulation of Stochastic Well-Formed Nets through symmetry exploitation

Stochastic Well-Formed Nets (SWN) is a High-Level Stochastic Petri Net formalism supporting performa-bility analysis. The symbolic marking and firing notions in SWNs allow to automatically aggregate states achieving significant reductions in highly symmetric models. If the reduced state space is still too large, simulation may be applied exploiting symbolic marking and firing to achieve more efficient handling of the Future Event List. This technique is implemented in the GreatSPN tool. In this paper symmetry based simulation methods are presented, their strong and weak points are discussed, the issue of performance indices definition and computation is introduced, and an extension exploiting the most recent results on partial symmetries is proposed.