Efficient Decomposition Algorithm for Stationary Analysis of Complex Stochastic Petri Net Models

Stochastic Petri nets are widely used for the modelling and analysis of non-functional properties of critical systems. The state space explosion problem often inhibits the numerical analysis of such models. Symbolic techniques exist to explore the discrete behaviour of even complex models, while block Kronecker decomposition provides memoryefficient representation of the stochastic behaviour. However, the combination of these techniques into a stochastic analysis approach is not straightforward. In this paper we compare various combinations of symbolic techniques and decomposition based analysis methods. Saturationbased exploration is used to build the state space representation and a new algorithm is introduced to efficiently build block Kronecker matrix representation to be used by the stochastic analysis algorithms. Measurements confirm that the novel combination of the two representations can expand the limits of previous approaches.