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, performance and performability. Many structural and stochastic extensions have been proposed in recent years to increase their modeling power, or their capability to handle large systems. This paper reviews recent developments by providing the theoretical background and the possible areas of application. Markovian Petri Nets are first considered together with very well established extensions known as Generalized Stochastic Petri Nets and Stochastic Reward Nets. Key ideas for coping with large state spaces are then discussed. The challenging area of non-Markovian 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 briefly discussed.