A Modeling Approach for Dynamically Reconfigurable Systems

Based on Markov reward models we introduce a modeling approach which allows us to describe the behavior of dynamical systems. We present an algorithm which evaluates and optimizes rather general configurations. We choose the expected cumulative reward over a finite time horizon as the optimality criterion. The method is applied to control and evaluate responsive queueing systems. The models are evaluated in the presence of subcomponent failures.

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