Construction and solution of Markov reward models

Stochastic Petri nets (SPNs) and extensions are a popular method for evaluating a wide variety of systems. In most cases, the interesting measures regarding the system's characteristics can be de ned at the net level by means of reward variables. Depending on the measures, these net-level reward models are solved either by rst generating a statelevel reward model or by directly generating paths from the net-level description. In this thesis, we propose algorithms for the generation of state-level reward models as well as for directly obtaining solutions from net-level reward models when the net-level reward models are speci ed as stochastic activity networks (SANs) with \step-based reward structure". Moreover, we propose algorithms for computing the expected value and the probability distribution function of a reward variable at speci ed time instants, and for computing the probability distribution function of reward accumulated during a nite interval. The interval may correspond to the mission period in a mission-critical system, the time between scheduled maintenances, or a warranty period; whereas the time instants may be critical instances during these intervals. The proposed algorithms avoid the construction of statelevel representations and the memory growth problems experienced when applying previous approaches to large models. Furthermore, we study the e ect of workload on the availability and response time of voting algorithms. Voting algorithms are a popular way to provide data consistency