论文标题
从深度学习的角度来固定优先级全球安排
Fixed Priority Global Scheduling from a Deep Learning Perspective
论文作者
论文摘要
最近，深度学习被认为是有效解决组合优化问题的可行解决方案之一，这些解决方案通常被认为是在各个研究领域中都具有挑战性的。在这项工作中，我们首先介绍了如何通过固定优先级全球计划（FPG）问题通过我们的初步工作来采用深度学习进行实时任务调度。然后，我们简要讨论了几种现实且复杂的FPG方案的深度学习采用的可能概括，例如，以依赖性，混合批判性任务调度来调度任务。我们认为，利用先进的深度学习技术有很多机会来提高各种系统配置和问题场景中的调度质量。
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.
