Solution bundles of Markov performability models through adaptive cross approximation

A technique to approximate solution bundles, i.e., solutions of a parametric model where parameters are treated as independent variables instead of constants, is presented for Markov models. Analyses based on an approximated solution bundle are more efficient than those that solve the model for all combinations of parameters' values separately. In this paper the idea is to properly adapt low rank tensor approximation techniques, and in particular Adaptive Cross Approximation, to the evaluation of performability attributes. Application on exemplary case studies confirms the advantages of the new solution technique with respect to solving the model for all time and parameters' combinations.