A divide and conquer approach for the performance evaluation of large stochastic Petri nets

State-space explosion is one of the main problems in the area of the performance evaluation of Petri net models. This problem prevents the exact numerical analysis of complex real-life systems. To overcome the limitation of state spaces that are too large, many methods have been proposed in which the whole system is divided into small analysable parts. This paper presents one of these so-called decomposition methods. In contrast to known decomposition methods, the whole model is decomposed automatically in the presented approach. Afterwards, the submodels are aggregated. The paper presents a new iterative aggregation method called MIMO (multiple input, multiple output) graph-based aggregation. In a third step, an iterative analysis is used to compute performance measures using iterative response-time approximation. The method is explained by applying it to an example. It is integrated in the TimeNET modelling and analysis tool for stochastic Petri nets.