An Optimization Framework for Real-Time Systems with Sustainable Schedulability Analysis
An Optimization Framework for Real-Time Systems with Sustainable Schedulability Analysis
复制标题
DOI：
10.1109/rtss49844.2020.00038
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Yecheng Zhao;Runzhi Zhou;Haibo Zeng
中科院分区：
文献类型：
--
作者：
Yecheng Zhao;Runzhi Zhou;Haibo Zeng
现代实时系统的设计不仅需要保证定时的正确性，而且还涉及到控制质量和能耗等其他关键指标。随着实时系统的日益复杂，迫切需要能够处理大规模系统的高效优化技术。然而，可调度性分析的复杂性通常使其难以直接纳入标准优化框架，并且对大量候选解决方案进行检查效率低下。本文提出了一种用于实时系统设计的新型优化框架。它利用了可调度性分析的可持续性，这种可调度性分析适用于大量实时系统。它构建了一个反例引导的迭代过程，以有效地从不可调度的解决方案中学习并排除许多类似的解决方案。与最先进的框架相比，提议的框架在提供相同质量的解决方案的同时可能要快十倍。
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.