Fast Decomposition in Large Stochastic Models

We propose a novel approach to the decomposition of large probabilistic models. The goal of our method is to avoid the evaluation of the subnetworks obtained by decomposition for all values of the state description vector, as would be necessary with a standard aggregation and decomposition approach. Instead, we propose a fixed-point iteration that requires the evaluation of the subnetwork for only a subset of the population levels. Outside the evaluated points, simple upper and lower linear approximations are used resulting in bounds for overall system performance measures. We concentrate the evaluation of the subnetworks in the regions where the difference between the lower and upper bound is most likely to impact the accuracy of the result.