RECENT DEVELOPMENTS IN NON-MARKOVIAN STOCHASTIC PETRI NETS

Analytical modeling plays a crucial role in the analysis and design of computer systems. Stochastic Petri Nets represent a powerful paradigm , widely used for such modeling in the context of dependability, perfo rmance and performability. Many structural and stochastic extensions h ave been proposed in recent years to increase their modeling power, or their capability to handle large systems. This paper reviews recent d evelopments by providing the theoretical background and the possible a reas of application. Markovian Petri Nets are first considered togethe r with very well established extensions known as Generalized Stochasti c Petri Nets and Stochastic Reward Nets. Key ideas for coping with lar ge state spaces are then discussed. The challenging area of non-Markov ian Petri nets is considered, and the related analysis techniques are surveyed together with the detailed elaboration of an example. Finally new models based on Continuous or Fluid Stochastic Petri Nets are bri efly discussed.