Analysis and optimization of soft error tolerance strategies for real-time systems

The safety of real-time embedded systems relies on both functional and timing correctness. On the timing side, realtime constraints are set on task executions, and missing them may lead to system failure. On the functional side, soft errors have become a major concern. Various soft error tolerance strategies are proposed for soft error detection and recovery, however they may introduce significant computation overhead and cause timing violations. In this work, we address the two aspects in an integrated framework, and propose a set of formulations to quantitatively model the impact of soft error detection and recovery mechanisms on real-time constraints. The formulations facilitate designers to analyze system feasibility under fault tolerance requirements and compare various architecture platforms. They may also help select the appropriate error tolerance mechanisms for software tasks, together with exploring task scheduling and allocation on representative single-core, multicore and distributed platforms, to maximize error coverage while meeting real-time constraints. Experiments on an industrial case study and synthetic examples demonstrate the effectiveness of our approach.