A formal approach to robustness maximization of complex heterogeneous embedded systems

Abstract

Embedded system optimization typically considers objectives such as cost, timing, buffer sizes and power consumption. Robustness criteria, i.e. sensitivity of the system to variations of properties like execution and transmission delays, input data rates, CPU clock rates, etc., has found less attention despite its practical relevance.In this paper we introduce robustness metrics and propose an algorithm considering these metrics in design space exploration and system optimization. The algorithm can optimize for static and for dynamic robustness, the latter including system or designer reactions to property variations. We explain several applications ranging from platform optimization to critical component identification.By means of extensive experiments we show that design space exploration pursuing classical design goals does not necessarily yield robust systems, and that our method leads to systems with significantly higher design robustness.

Multi-mode monitoring for mixed-criticality real-time systems

“…[11,20,6,21,19] derives maximal parameter variations under which constraints are still satisfied. Thus, when task activation pattern bounds are considered as parameters, sensitivity analysis can be understood as derivation of maximal assume interfaces of a component.…”

“…When multiple parameters are regarded simultaneously (i.e. multi-dimensional sensitivity analysis) the derived bounds can become paretooptimal [11], i.e. bounds are incomparable and are not dominated by another bound.…”

“…5 any specification of activation pattern bounds, under which all deadlines are met, provides a consistent assume interface tuple. Several consistent interface tuples for a system may exist [11] and may be incomparable according to the pre-order from Def. 4, i.e.α A α A .…”

“…As the interface tuples are incomparable they reflect a pareto-front [11] of activation pattern bounds. We will address the determination of such interface tuples in Sec.…”

Methods for multi-dimensional robustness optimization in complex embedded systems

“…For that purpose we need formal robustness metrics that allow to quantify robustness that are then used in design space exploration. The approach presented in [5] defines robustness metrics using onedimensional sensitivity analysis techniques. However, since the underlying sensitivity analysis is one-dimensional these robustness metrics ignore dependencies between the considered system properties.…”