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

TL;DR

This paper proposes a machine learning framework that estimates data center power consumption by analyzing acoustic signals from cooling fans, achieving over 85% accuracy.

Contribution

It introduces a novel acoustic side channel approach combined with neural networks for power estimation in data centers, addressing uncertainty in energy modeling.

Findings

Achieved over 85% accuracy in power estimation.

Utilized acoustic signals as a non-intrusive measurement method.

Demonstrated effectiveness of neural networks in this context.

Abstract

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.