Towards Contrastive and Data Driven Cloud Data Center Energy Prediction using Regression Analysis

Abstract

Abstract: Data centers are the backbone of todays Internet and cloud computing systems. Due to the increasing demand for electrical energy by data centers, it is necessary to account for the vast amount of energy they consume. Energy modeling and prediction of data centers plays a pivotal role in this context. In this study, we address the challenge of predicting energy consumption in cloud data centers, crucial for managing their significant electricity demand. Despite numerous existing methods, there remains a lack of robust methodologies. To fill this gap, we propose a novel approach that incorporates aleatoric uncertainty estimation. Our method utilizes regression distributions to model this uncertainty, with parameters derived from regressive techniques. This yields energy consumption predictions as random variables drawn from these distributions. Additionally, we illustrate how these random variables can be aggregated to form probabilistic forecasts for diverse data center portfolios. Our methodology achieves three key advancements: 1) introducing a simple multiple linear regression model for fundamental series; 2) devising a unique method that combines quantile regression and empirical copulas to estimate joint distribution; 3) enhancing prediction accuracy through a weighted correction technique based on constrained quantile regression.