Embedded system optimization typically considers objectives such as cost, timing, buffer sizes, and power consumption. Robustness criteria, i.e. sensitivity of the system to property variations like execution and transmission delays, input data rates, CPU clock rates, etc., has found less attention despite its practical relevance.
In this paper we present an approach for optimizing multi-dimensional robustness criteria in complex distributed embedded systems. The key novelty of our approach is a scalable stochastic multi-dimensional sensitivity analysis technique approximating the sought-after sensitivity front from two sides, i.e. coming from the space of working and from the space of non-working system property combinations.
We utilize the proposed stochastic sensitivity analysis to derive multi-dimensional robustness metrics, which are capable of bounding the robustness of given system configurations with little computational effort.
The proposed metrics can significantly speed up multi-dimensional robustness optimization by quickly identifying promising system configurations, whose in-depth robustness evaluation can be performed subsequently to the optimization process.
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