Design optimization for real-time systems with sustainable schedulability analysis
Design optimization for real-time systems with sustainable schedulability analysis
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DOI：
10.1007/s11241-022-09388-5
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发表时间：
2022-08
影响因子：
1.3
通讯作者：
Yecheng Zhao;Runzhi Zhou;Haibo Zeng
中科院分区：
文献类型：
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作者：
Yecheng Zhao;Runzhi Zhou;Haibo Zeng
现代实时系统的设计不仅需要保证其时序的正确性，而且还涉及其他关键指标，如控制质量和能耗。随着实时系统变得越来越复杂，迫切需要能够处理大规模系统的有效优化技术。然而，可并行性分析的复杂性往往使得其难以直接并入标准优化框架中，并且针对大量候选解进行检查是低效的。在本文中，我们提出了一种新的优化框架的设计实时系统。它利用了可扩展性分析的可持续性，可扩展性分析适用于一大类实时系统。它建立了一个反例引导的迭代过程，以有效地从一个不可解释的解决方案中学习，并排除许多类似的解决方案。与最先进的技术相比，所提出的框架可能快十倍，同时提供具有相同质量的解决方案。这项工作是RTSS 2020上发表的会议论文的期刊扩展，它增加了对提高算法可扩展性的技术的新讨论，以及一组新的实验，以更好地评估所提出的框架。
The design of modern real-time systems not only needs to guarantee their timing correctness, but also involves other critical metrics such as control quality and energy consumption. As real-time systems become increasingly complex, there is an urgent need for efficient optimization techniques that can handle large-scale systems. However, the complexity of schedulability analysis often makes it difficult to be directly incorporated in standard optimization frameworks, and inefficient to be checked against a large number of candidate solutions. In this paper, we propose a novel optimization framework for the design of real-time systems. It leverages the sustainability of schedulability analysis that is applicable for a large class of real-time systems. It builds a counterexample-guided iterative procedure to efficiently learn from an unschedulable solution and rule out many similar ones. Compared to the state-of-the-art, the proposed framework may be ten times faster while providing solutions with the same quality. This work is a journal extension to the conference paper published at RTSS 2020, which adds new discussions for techniques that improve the algorithm scalability, as well as a set of new experiments to better evaluate the proposed framework.