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Due to destructive effects like temperature and radiation, today's embedded systems have to deal with unreliable components. The intensity of these effects depends on uncertain aspects like environmental or usage conditions such that highly safety-critical systems are pessimistically designed for worst-case mission profiles. These uncertain aspects may affect several components simultaneously, implying correlation across uncertainties in their reliability. This paper enables a state-of-the-art uncertainty-aware reliability analysis technique to consider multiple arbitrary correlations; in other words, components' reliability is affected by several uncertain aspects to different degrees. This analysis technique combines reliability models such as binary decision diagrams with a Monte Carlo simulation, and derives the uncertainty distribution of the system's reliability with insights on the mean, quantile intervals, and so on. The proposed correlation method aims at generating correlated samples from the uncertainty distribution of components' reliability such that the shape and statistical properties of each individual distribution remain unchanged. Experimental results confirm that the proposed correlation model enables the employed uncertainty-aware analysis to accurately calculate uncertainty at system level.

Real-world problems typically require the simultaneous optimization of several, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions, which further increases the complexity of optimization. To cope with such uncertainties, robust optimization is widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions or sampled data. However, existing techniques mostly either fail to consider the actual distributions or assume uncertainty as instances of uniform or Gaussian distributions. This paper introduces an empirical approach that enables an efficient comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. Given two candidate solutions under comparison, this operator calculates the probability that one solution dominates the other in terms of each uncertain objective. It can substitute for the standard comparison operator of existing optimization techniques such as evolutionary algorithms to enable discovering robust solutions to problems with multiple uncertain objectives. This paper also proposes to incorporate various uncertainties in well-known multi-objective problems to provide a benchmark for evaluating uncertainty-aware optimization techniques. The proposed comparison operator and benchmark suite are integrated into an existing optimization tool that features a selection of multi-objective optimization problems and algorithms. Experiments show that in comparison with existing techniques, the proposed approach achieves higher optimization quality at lower overheads.

In recent years, reliability has become a major issue and objective during the design of embedded systems. Here, different techniques to increase reliability like hardware-/softwarebased redundancy or component hardening are applied systematically during Design Space Exploration (DSE), aiming at achieving highest reliability at lowest possible cost. Existing approaches typically solely provide reliability measures, e. g. failure rate or Mean-Time-To-Failure (MTTF), to the optimization engine, poorly guiding the search which parts of the implementation to change. As a remedy, this work proposes an efficient approach that (a) determines the importance of resources with respect to the system's reliability and (b) employs this knowledge as part of a local search to guide the optimization engine which components/design decisions to investigate. First, we propose a novel approach to derive Importance Measures (IMs) using a structural evaluation of Success Trees (STs). Since ST-based reliability analysis is already used for MTTF calculation, our approach comes at almost no overhead. Second, we enrich the global DSE with a local search. Here, we propose strategies guided by the IMs that directly change and enhance the implementation. In our experimental setup, the available measures to enhance reliability are the selection of hardening levels during resource allocation and software-based redundancy during task binding; exemplarily, the proposed local search considers the selected hardening levels. The results show that the proposed method outperforms a state-of-the-art approach regarding optimization quality, particularly in the search for highly-reliable yet affordable implementationsat negligible runtime overhead.