Kuan-Hsun Chen, Björn Bönninghoff, Jian-Jia Chen, Peter Marwedel (TU Dortmund)
Software-based error detection and compensation techniques have been widely studied to avoid unrecoverable system failures on embedded systems caused by soft-errors. Methods like error-correction codes and redundant execution can offer high flexibility without the needs of special hardware supports. However, such software-based approaches may lead to system overload due to the execution time overhead. Therefore, an adaptive deployment of such techniques to meet both application requirements and system constraints is desired. From our case studies, we observe that a control task can tolerate limited errors with acceptable performance loss. Such tolerance can be modeled as a (m, k) constraint which requires at least m correct runs out of any k consecutive runs to be correct. In this work, we discuss how a given (m, k) constraint can be satisfied by adopting patterns of task instances with error detection and compensation capabilities. We introduce static strategies with a schedulability analysis, and enhance them with an online adaption that increases efficiency while preserving analysis results. Throughout a real-world case study as well as synthesized task sets, we show the effectiveness of our methods.