首页> 外文会议>15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications >An Effective Reliability Goal Assurance Method Using Geometric Mean for Distributed Automotive Functions on Heterogeneous Architectures
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An Effective Reliability Goal Assurance Method Using Geometric Mean for Distributed Automotive Functions on Heterogeneous Architectures

机译:基于几何均值的异构架构上分布式汽车功能的有效可靠性目标保证方法

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摘要

Functional safety is aimed at avoiding unacceptable risks and safety damages due to system functional failures, and it is a critical demand for the automotive embedded systems. For safety-critical distributed automotive functions, reliability is an important functional safety requirement and reliability goal should be assured. In general, the key of reliability goal assurance method is to transfer the reliability goal of a distributed function to that of each task. This study proposes an effective reliability goal assurance method called RGAGM for automotive functional safety. The core idea of this method is defining two kinds of geometric mean for tasks and function, respectively, and preassigning geometric mean-based reliability values for unassigned tasks, thereby saving more resources for systems. The correctness of the proposed RGAGM method is proved. Experiment results on the real-life automotive function and the randomly generated distributed automotive functions show that the proposed method can effectively ensure the reliability goal and reduce resource consumption cost compared with the state-of-the-art MRCRG method.
机译:功能安全旨在避免由于系统功能故障而导致的不可接受的风险和安全损失,这是对汽车嵌入式系统的关键要求。对于安全性至关重要的分布式汽车功能,可靠性是一项重要的功能安全要求,应确保达到可靠性目标。通常,可靠性目标保证方法的关键是将分布式功能的可靠性目标转移到每个任务的可靠性目标。这项研究提出了一种有效的可靠性目标保证方法,称为RGAGM,用于汽车功能安全。该方法的核心思想是分别为任务和功能定义两种几何平均值,并为未分配的任务预先分配基于几何平均值的可靠性值,从而为系统节省更多资源。证明了所提出的RGAGM方法的正确性。对实际汽车功能和随机生成的分布式汽车功能的实验结果表明,与最新的MRCRG方法相比,该方法可以有效地确保可靠性目标并降低资源消耗成本。

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