  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.
