Abstract
The work described in this paper develops a methodology for modelling and analysis of complex timed systems. The methodology is based on the Stochastic Reward Nets (SRN) [1]–[4] formalism, which has gained its popularity by its ability to enable the studying of the performability, reliability and availability of, e.g., computer and communication systems. SRN is an extension of Generalized Stochastic Petri Nets (GSPN) and it is supported by such tools as the Stochastic Petri Net Package (SPNP) [5]–[6]. Power and accuracy in the application of SRN derive from mapping a model into a corresponding Markov Reward Model (MRM) from which quantitative measures are extracted in terms of reward functions associated to tangible markings of the source model. To cope with large models which cause state explosions in the underlying MRM, simulations are used [2].
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