Learning-Based Response Time Analysis in Real-Time Embedded Systems: A Simulation-Based Approach
Learning-Based Response Time Analysis in Real-Time Embedded Systems: A Simulation-Based Approach
复制标题
实时嵌入式系统中基于学习的响应时间分析：基于仿真的方法
DOI：
10.1145/3194095.3194097
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
B. Lisper
中科院分区：
文献类型：
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作者：
M. H. Moghadam;Mehrdad Saadatmand;Markus Borg;M. Bohlin;B. Lisper
响应时间分析是验证实时系统行为的一项重要任务。已经提出了几种响应时间分析方法来解决这一挑战，特别是对于具有不同复杂程度的实时系统。静态分析是一种流行的方法，在这种情况下，但它的实用性是有限的，由于工业实时系统的高度复杂性，以及在这些系统中的许多不可预测的运行时事件。在这篇正在进行的论文中，我们提出了一种基于模拟的响应时间分析方法，使用强化学习来找到导致最坏情况响应时间的执行场景。该方法学习如何提供一个实际的估计，最坏情况下的响应时间，通过模拟程序，而不执行静态分析。我们的初步研究表明，所提出的方法可以适用于工业实时控制系统的仿真环境中，以提供一个实际的估计的执行情况，导致最坏情况下的响应时间。
Response time analysis is an essential task to verify the behavior of real-time systems. Several response time analysis methods have been proposed to address this challenge, particularly for real-time systems with different levels of complexity. Static analysis is a popular approach in this context, but its practical applicability is limited due to the high complexity of the industrial real-time systems, as well as many unpredictable runtime events in these systems. In this work-in-progress paper, we propose a simulation-based response time analysis approach using reinforcement learning to find the execution scenarios leading to the worst-case response time. The approach learns how to provide a practical estimation of the worst-case response time through simulating the program without performing static analysis. Our initial study suggests that the proposed approach could be applicable in the simulation environments of the industrial real-time control systems to provide a practical estimation of the execution scenarios leading to the worst-case response time.