Methods for multi-dimensional robustness optimization in complex embedded systems
EMSOFT '07 Proceedings of the 7th ACM & IEEE international conference on Embedded software
Generation and calibration of compositional performance analysis models for multi-processor systems
SAMOS'09 Proceedings of the 9th international conference on Systems, architectures, modeling and simulation
Computing robustness of FlexRay schedules to uncertainties in design parameters
Proceedings of the Conference on Design, Automation and Test in Europe
Robust design of embedded systems
Proceedings of the Conference on Design, Automation and Test in Europe
Optimization of task allocation and priority assignment in hard real-time distributed systems
ACM Transactions on Embedded Computing Systems (TECS)
Efficient constraint handling during designing reliable automotive real-time systems
Ada-Europe'12 Proceedings of the 17th Ada-Europe international conference on Reliable Software Technologies
Proceedings of the 14th ACM SIGPLAN/SIGBED conference on Languages, compilers and tools for embedded systems
Design synthesis and optimization for automotive embedded systems
Proceedings of the 2014 on International symposium on physical design
| Hi-index | 0.00 |
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.