On Markov reward modelling with FSPNs

In this paper, fluid stochastic Petri nets (FSPNs) will be used for modelling reward in a performability model. Two variations of a known performability model are presented in order to demonstrate the ability of FSPNs in modelling accumulated rate reward as well as accumulated impulse reward. In the first model two fluid places are used, one of which represents the profit (reward) obtained by operating the system and the other one the buffer that is approximated continuously. In the second model only one fluid place is used, representing the costs (negative reward) arising due to repair of system components. The costs increase continuously at deterministic rate while the system is in state of repair (which is a rate reward in the model). Additional costs incur each time the buffer fails (which is an impulse reward in the model). With a numerical solution algorithm the distribution of the reward and its mean are computed. The accuracy of the numerical algorithm is studied by showing for the first model the impact of the choice of the discretization stepsizes on the obtained solution. Different boundary conditions are discussed for the second model.