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首页> 外文期刊>IEEE transactions on industrial informatics >Autonomous Power Management With Double-Q Reinforcement Learning Method
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Autonomous Power Management With Double-Q Reinforcement Learning Method

机译：双Q加固学习方法自主电源管理

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摘要

Energy efficiency and autonomous power management are extremely important for mobile-edge computing. Reducing energy consumption of a number of applications running concurrently in mobile devices while maintaining performance poses a challenge to energy optimization due to the limited capacity of the embedded battery. To extend battery life and offer a long-lasting working energy, dynamic voltage and frequency scaling (DVFS) has been widely used in mobile devices for energy consumption minimization. However, most conventional DVFS techniques scale operating frequency based on static policies, and thus, they are difficult to be adapted to systems of varied conditions. In order to improve adaptivity, in this article, we proposed a Double-Q power management approach to scale operating frequency based on learning. The Double-Q method stores two Q tables and two corresponding update functions. In each decision point, either of Q tables is randomly chosen and updated, while the other is used for the measurement. This mechanism reduces the overestimation in Q values, consequently enhancing the accurateness of frequency predictions. To evaluate the effectiveness of our proposed approach, a Double-Q governor is implemented in the Linux kernel. Our approach is computationally light, and experimental results indicate that it achieves at least 5-18% total energy saving compared to ondemand and conservative governors, as well as Q learning-based method.
机译：能源效率和自主电源管理对移动边缘计算非常重要。减少在移动设备中同时运行的许多应用程序的能量消耗，同时保持性能对能量优化的挑战由于嵌入式电池的容量有限。为了延长电池寿命并提供长持久的工作能源，动态电压和频率缩放（DVFS）已广泛用于移动设备以进行能耗最小化。然而，大多数传统的DVFS技术基于静态策略的操作频率，因此，它们难以适应各种条件的系统。为了提高适应性，在本文中，我们提出了一种基于学习的双Q电源管理方法来规模运行频率。双Q方法存储两个Q表和两个相应的更新功能。在每个决策点中，Q表中的任何一个都被随机选择和更新，而另一个是用于测量的。该机制降低了Q值的高度估计，从而提高了频率预测的准确性。为了评估我们提出的方法的有效性，在Linux内核中实现了一个双Q调速器。我们的方法是计算光，实验结果表明，与达梅兰和保守州长，以及基于Q学习的方法相比，它达到了至少5-18％的总节能。

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