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.

现代实时系统的设计不仅需要保证其时序正确性，还涉及控制质量和能耗等其他关键指标。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高效优化技术。然而，可调度性分析的复杂性往往使其难以直接纳入标准优化框架，并且针对大量候选解决方案进行检查效率低下。在本文中，我们提出了一种用于实时系统设计的新颖优化框架。它利用了适用于一大类实时系统的可调度性分析的可持续性。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。与最先进的技术相比，所提出的框架在提供相同质量的解决方案的同时可能快十倍。这项工作是 RTSS 2020 上发表的会议论文的期刊扩展，其中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。