从深度学习角度看固定优先级全局调度
Fixed Priority Global Scheduling from a Deep Learning Perspective
摘要：
深度学习最近被认为是有效解决组合优化问题的可行解决方案之一，这些问题在各种研究领域中通常被认为是重要但具有挑战性的。在这项工作中，我们首先介绍了如何采用深度学习的实时任务调度，通过我们的固定优先级全局调度（FPGS）问题的初步工作。然后，我们简要讨论了深度学习在几个现实和复杂的FPGS场景中的可能推广，例如，具有依赖性的任务调度、混合关键性任务调度。我们相信有很多机会利用先进的深度学习技术来提高各种系统配置和问题场景中的调度质量。
英文摘要：
Deep Learning has been recently recognized as one of the feasible solutions to effectively address combinatorial optimization problems, which are often considered important yet challenging in various research domains. In this work, we first present how to adopt Deep Learning for real-time task scheduling through our preliminary work upon fixed priority global scheduling (FPGS) problems. We then briefly discuss possible generalizations of Deep Learning adoption for several realistic and complicated FPGS scenarios, e.g., scheduling tasks with dependency, mixed-criticality task scheduling. We believe that there are many opportunities for leveraging advanced Deep Learning technologies to improve the quality of scheduling in various system configurations and problem scenarios.
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