A General and Scalable Method for Optimizing Real-Time Systems with Continuous Variables
A General and Scalable Method for Optimizing Real-Time Systems with Continuous Variables
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DOI：
10.1109/rtas58335.2023.00017
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Sen Wang;Ryan K. Williams;Haibo Zeng
中科院分区：
文献类型：
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作者：
Sen Wang;Ryan K. Williams;Haibo Zeng
在实时系统的优化设计中，设计人员经常面临一个具有挑战性的问题，即可调度性条件是非凸的、非连续的，或者缺乏解析形式来理解其性质。在本文中，我们提出了一个通用的和可扩展的框架，优化实时系统，称为数值优化器与实时高光（NORTH）。NORTH将可调度性分析视为一个黑盒，它可能只会在系统可调度性上返回true/false结果。在基于梯度的数值优化文献中的活动集方法的基础上，NORTH提出了管理活动约束的新方法，以进一步改进基于梯度的优化器。我们将提出的方法应用于两个实例问题，一个是动态电压和频率缩放系统的能量优化问题，另一个是控制性能的优化问题。实验结果表明，该框架的运行速度比目前最先进的方法快102到105倍，同时保持相似的解决方案质量。
In the optimization of real-time systems, designers often face a challenging problem where the schedulability conditions are non-convex, non-continuous, or lack an analytical form to understand their properties. In this paper, we propose a general and scalable framework for optimizing real-time systems, named Numerical optimizer with Real-Time Highlight (NORTH). NORTH treats schedulability analysis as a blackbox which may only return true/false results on system schedulability. Built upon the active-set methods from the gradient-based numerical optimization literature, NORTH proposes new methods to manage active constraints to further improve the gradient-based optimizers. We apply the proposed approach to two example problems, one on energy optimization for systems with dynamic voltage and frequency scaling, and the other on the optimization of control performance. Experimental results demonstrate that the proposed framework runs 102 to 105 times faster than state-of-the-art methods while maintaining similar solution quality.