论文标题
基于机器学习的框架，用于使用声学侧渠道估算数据中心功率
Machine Learning Based Framework for Estimation of Data Center Power Using Acoustic Side Channel
论文作者
论文摘要
数据中心是高功率消费者，尽管为提高能源效率做出了所有努力，但数据中心的能源消耗仍在上升。数据中心中对能源意识的需求使得使用电源建模和估计仍然是一个巨大的挑战，因为该领域的不确定性很大。在本文中，提出了一种基于机器学习的方法，以通过使用服务器室中基于风扇的冷却系统中的风扇引起的声学侧通道来概述估计功耗量。为此，通过麦克风在服务器室中记录的声学信号的频率组件被提取，预处理并馈送到多层神经网络作为估计器中。提出的方法表现良好，以估计功耗超过85％的精度。
Data centers are high power consumers and the energy consumption of data centers keeps on rising in spite of all the efforts for increasing the energy efficiency. The need for energy-awareness in data centers makes the use of power modeling and estimation to be still a big challenge due to huge amount of uncertainty in this area. In this paper, a machine learning based method is proposed to approximately estimate the amount of power consumption by using acoustic side channel caused by fan in the fan-based cooling system in the server room. For doing so, frequency components of the acoustic signal, recorded by a microphone in the server room, is extracted, pre-processed, and fed to a Multi-Layer Neural-Network as an estimator. The proposed method performed well to estimate the power consumption, having more than 85 percent accuracy.
