基于机器学习的数据中心侧信道功率估计框架
Machine Learning Based Framework for Estimation of Data Center Power Using Acoustic Side Channel
摘要：
数据中心是高功率消耗者，并且数据中心的能量消耗持续上升，尽管为提高能量效率做出了所有努力。数据中心对能源意识的需求使得功率建模和估计的使用仍然是一个巨大的挑战，因为这一领域存在大量的不确定性。本文提出了一种基于机器学习的方法来近似估计的功率消耗量，通过使用在风扇冷却系统中的服务器机房中的风扇引起的声学侧通道。为此，由服务器室中的麦克风记录的声学信号的频率分量被提取、预处理并馈送到作为估计器的多层神经网络。所提出的方法表现良好，估计功耗，具有超过85%的准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
