Data Center Analytics Platform for Efficient Power Usage

Modern Data Centers (DCs) require large amount of power in order to operate their underlying IT infrastructure and the surrounding support systems, such as cooling systems and power conversion systems, due to the complex interdependencies among these support systems. This situation requires a strategy for using the power efficiently while operating a DC. In this study, we firstly propose a software system that serves as data storage for both real-time and offline sensor data about power usage in a DC. Secondly, we provide an analytical platform that uses state-of-the-art deep learning techniques to make predictive data analytics and custom recommendations to DC operators in order to improve power usage effectiveness (PUE) in a DC. The aimed improvement involves estimating the conditions for achieving a PUE rate of 1.03 within an error range of 0.4 percent.