Representation and analysis of behavior for multiprocess systems by using stochastic Petri nets

This paper proposes a new approach to visually represent the behavior of multiprocess in a computer network system using stochastic Petri net (SPN), and an aggregate approach of SPN and Markov renewal process (MPR) to conduct behavior analysis and performance evaluation for the system. SPN is employed because of its highly visual nature that can give insight into the nature of the modeled system and because of its expressive power for an exponentially distributed event. In order to increase the analytical power of the SPN model, MRP is introuced, and an embedded transference probability matrix is applied to obtain the steady-state solution of the model, from which it is possible to obtain automatically the performance measures of the multiprocess computer network system.