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Data centers are high power consumers and the energy consumption of datacenters keeps on rising in spite of all the efforts for increasing the energyefficiency. The need for energy-awareness in data centers makes the use ofpower modeling and estimation to be still a big challenge due to huge amount ofuncertainty in this area. In this paper, a machine learning based method isproposed to approximately estimate the amount of power consumption by usingacoustic side channel caused by fan in the fan-based cooling system in theserver room. For doing so, frequency components of the acoustic signal,recorded by a microphone in the server room, is extracted, pre-processed, andfed to a Multi-Layer Neural-Network as an estimator. The proposed methodperformed well to estimate the power consumption, having more than 85 percentaccuracy.

Machine Learning Based Framework for Estimation of Data Center Power Using Acoustic Side Channel