Grishina, A.; Chinnici, M.; Kor, A.-L.; Rondeau, E.; Georges, J.-P. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. Energies2020, 13, 4378.
Grishina, A.; Chinnici, M.; Kor, A.-L.; Rondeau, E.; Georges, J.-P. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. Energies 2020, 13, 4378.
Grishina, A.; Chinnici, M.; Kor, A.-L.; Rondeau, E.; Georges, J.-P. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. Energies2020, 13, 4378.
Grishina, A.; Chinnici, M.; Kor, A.-L.; Rondeau, E.; Georges, J.-P. A Machine Learning Solution for Data Center Thermal Characteristics Analysis. Energies 2020, 13, 4378.
Abstract
Energy efficiency of Data Center (DC) operations heavily relies on IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of IoT- enabled smart systems. Consequently, increased demand for computing power will bring about increased waste heat dissipation in data centers. In order to bring about a DC energy efficiency, it is imperative to explore the thermal characteristics analysis of an IT room (due to waste heat). This work encompasses the employment of an unsupervised machine learning modelling technique for uncovering weaknesses of the DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for energy efficiency improvement that will feed into DC recommendations. The methodology employed for this research includes statistical analysis of IT room thermal characteristics, and the identification of individual servers that frequently occur in the hotspot zones. A critical analysis has been conducted on available big dataset of ambient air temperature in the hot aisle of ENEA Portici CRESCO6 computing cluster. Clustering techniques have been used for hotspots localization as well as categorization of nodes based on surrounding air temperature ranges. The principles and approaches covered in this work are replicable for energy efficiency evaluation of any DC and thus, foster transferability. This work showcases applicability of best practices and guidelines in the context of a real commercial DC that transcends the set of existing metrics for DC energy efficiency assessment.
Keywords
Data Center; Thermal Characteristics Analysis; Machine Learning, Energy Efficiency, Hotspots, Clustering Technique, Unsupervised Learning
Subject
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
