Uncertainty-aware reliability analysis and optimization

Due to manufacturing tolerances and aging effects, future embedded systems have to cope with unreliable components. The intensity of such effects depends on uncertain aspects like environmental or usage conditions such that highly safety-critical systems are pessimistically designed for worst-case mission profiles. In this work, we propose to explicitly model the uncertain characteristics of system components, i. e. we model components using reliability functions with parameters distributed between a best and worst case. Since destructive effects like temperature may affect several components simultaneously (e. g. those in the same package), a correlation between uncertainties of components exists. The proposed uncertainty-aware method combines a formal analysis approach and a Monte Carlo simulation to consider uncertain characteristics and their different correlations. It delivers a holistic view on the system's reliability with best/worst/average-case behavior and also insights on variance and quantiles. But, existing optimization approaches typically assume design objectives to be single values or to follow a predefined distribution. As a remedy, we propose a dominance criterion for meta-heuristic optimization approaches like evolutionary algorithms that enables the comparison of system implementations with arbitrarily distributed characteristics. Our presented experimental results show that (a) the proposed analysis comes at low overhead while capturing existing uncertainties with sufficient accuracy, and (b) the optimization process is significantly enhanced when guiding the search process by additional aspects like variance and the 95% quantile, delivering better system implementations as found by an uncertainty-oblivious optimization approach.