Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.