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 ne t (SPN), and an aggregate approach of SPN and Markov renewal process ( MRP) to conduct behavior analysis and performance evaluation for the s ystem. SPN is employed because of its highly visual nature that can gi ve insight into the nature of the modeled system and because of its ex pressive power for an exponentially distributed event. In order to inc rease the analytical power of the SPN model, MRP is introduced, and an embedded transference probability matrix is applied to obtain the ste ady-state solution of the model, from which it is possible to obtain a utomatically the performance measures of the multiprocess computer net work system.