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Maximal software execution time: a regression-based approach

机译:最大软件执行时间:基于回归的方法

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This work aims at facilitating the schedulability analysis of non-critical systems, in particular those that have soft real-time constraints, where worst-case execution times (WCETs) can be replaced by less stringent probabilistic bounds, which we call maximal execution times (METs). To this end, it is possible to obtain adequate probabilistic execution time models by separating the non-random dependency on input data from a modeling error that is purely random. The proposed approach first utilizes execution time multivariate measurements for building a multiple regression model and then uses the theory related to confidence bounds of coefficients, in order to estimate the upper bound of execution time. Although certainly our method cannot directly achieve extreme probability levels that are usually expected for WCETs, it is an attractive alternative for MET analysis, since it can arguably guarantee safe probabilistic bounds. The method’s effectiveness is demonstrated on a JPEG decoder running on an industrial SPARC V8 processor.
机译:这项工作旨在促进非关键系统的调度分析,特别是那些具有软实时约束的那些,其中最坏情况执行时间(WCET)可以替换为较少的严格概率范围,我们调用最大执行时间(大都会。为此,可以通过从纯粹随机的建模错误中分离对输入数据的非随机依赖性来获得足够的概率执行时间模型。所提出的方法首先利用用于构建多元回归模型的执行时间多变量测量,然后使用与系数的置信范围相关的理论,以估计执行时间的上限。虽然我们的方法无法直接实现WCET的预期通常预期的极端概率水平,但它是符合符合分析的有吸引力的替代方案,因为它可以可以可以可以可以保证安全的概率范围。该方法的有效性在工业SPARC V8处理器上运行的JPEG解码器上展示。

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