Predicting Container-Level Power Consumption in Data Centers using Machine Learning Approaches

Abstract: Due to the ongoing climate crisis, reducing waste and carbon emissions has become hot topic in many fields of study. Cloud data centers contribute a large portion to the world’s energy consumption. In this work, methodologies are developed using machine learning algorithms to improve prediction of the energy consumption of a container in a data center. The goal is to share this information with the user ahead of time, so that the same can make educated decisions about their environmental footprint.This work differentiates itself in its sole focus on optimizing prediction, as opposed to other approaches in the field where energy modeling and prediction has been studied as a means to building advanced scheduling policies in data centers. In this thesis, a qualitative comparison between various machine learning approaches to energy modeling and prediction is put forward. These approaches include Linear, Polynomial Linear and Polynomial Random Forest Regression as well as a Genetic Algorithm, LSTM Neural Networks and Reinforcement Learning. The best results were obtained using the Polynomial Random Forest Regression, which produced a Mean Absolute Error of of 26.48% when run against data center metrics gathered after the model was built. This prediction engine was then integrated into a Proof of Concept application as an educative tool to estimate what metrics of a cloud job have what impact on the container power consumption.