Abstract
The need for a more accurate modeling of the performance of systems whose functioning mainly dependant on external time parameters such as the number of requests during a particular time phase, led us to a novel approach, taking into account the time parameters involved. This is achieved through the evaluation of a performability indicator modeled by means of a two-phase cyclic nonhomogenous Markov chain considering periodical time-dependant arrival request probabilities and applied to a replicated database system. The computation of the performability indicator modeled by cyclic nonhomogeneous Markov chain requires the use of efficient computational methods by using explicit approximate inverse preconditioning methods.
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