Backward Coupling in Bounded Free-Choice Nets Under Markovian and Non-Markovian Assumptions

Anne Bouillard (1) , Bruno Gaujal (2)

1 DISTRIBCOM - Distributed and Iterative Algorithms for the Management of Telecommunications Systems
2 MESCAL - Middleware efficiently scalable

In this paper, we show how to design a perfect sampling algorithm for stochastic Free-Choice Petri nets by backward coupling. For Markovian event graphs, the simulation time can be greatly reduced by using extremal initial states, namely blocking marking, although such nets do not exhibit any natural monotonicity property. Another approach for perfect simulation of non-Markovian event graphs is based on a (max,plus) representation of the system and the theory of (max,plus) stochastic systems. We also show how to extend this approach to one-bounded free choice nets to the expense of keeping all states. Finally, experimental runs show that the (max,plus) approach needs a larger simulation time than the Markovian approach.

Domaines: Calcul parallèle, distribué et partagé [cs.DC]

Anne Bouillard, Bruno Gaujal. Backward Coupling in Bounded Free-Choice Nets Under Markovian and Non-Markovian Assumptions. Discrete Event Dynamic Systems, 2008, 18, pp.473-498. ⟨10.1007/s10626-008-0041-8⟩. ⟨hal-00953608⟩