Reward model solution methods with impulse and rate rewards: An algorithm and numerical results

Abstract

Reward models have become an important method for specifying performability models for many types of systems. Many methods have been proposed for solving these reward models, but no method has proven itself to be applicable over all system classes and sizes. Furthermore, specification of reward models has usually been done at the state level, which can be extremely cumbersome for realistic models. We describe a method to specify reward models as stochastic activity networks (SANs) with impulse and rate rewards, and a method by which to solve these models via randomization. The method is an extension of one proposed by de Souza e Silva and Gail in which impulse and rate rewards are specified at the SAN level, and solved in a single model. Furthermore, a novel method of discarding trajectories of low probabilities with algorithms to compute bounds on the injected error is proposed. The methodology is presented, together with the results on the time and space efficiency of a particular implementation.