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A Machine Learning Solution for Data Center Thermal Characteristics Analysis

Author

Listed:
  • Anastasiia Grishina

    (Department of Mathematics and Computer Science, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands)

  • Marta Chinnici

    (Department of Energy Technologies and Renewable Sources, ICT Division, ENEA Casaccia Research Center, 00123 Rome, Italy)

  • Ah-Lian Kor

    (School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UK)

  • Eric Rondeau

    (Université de Lorraine, Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Automatique de Nancy (CRAN), F-54000 Nancy, France)

  • Jean-Philippe Georges

    (Université de Lorraine, Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Automatique de Nancy (CRAN), F-54000 Nancy, France)

Abstract

The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its 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 smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further feeds into DC recommendations. With the aim to identify overheated zones in a DC IT room and corresponding servers, we applied analyzed thermal characteristics of the IT room. Experimental dataset includes measurements of ambient air temperature in the hot aisle of the IT room in ENEA Portici research center hosting the CRESCO6 computing cluster. We use machine learning clustering techniques to identify overheated locations and categorize computing nodes based on surrounding air temperature ranges abstracted from the data. This work employs the principles and approaches replicable for the analysis of thermal characteristics of any DC, thereby fostering transferability. This paper demonstrates how best practices and guidelines could be applied for thermal analysis and profiling of a commercial DC based on real thermal monitoring data.

Suggested Citation

  • Anastasiia Grishina & Marta Chinnici & Ah-Lian Kor & Eric Rondeau & Jean-Philippe Georges, 2020. "A Machine Learning Solution for Data Center Thermal Characteristics Analysis," Energies, MDPI, vol. 13(17), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4378-:d:403649
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    References listed on IDEAS

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    1. Damián Fernández-Cerero & Alejandro Fernández-Montes & Francisco Velasco, 2018. "Productive Efficiency of Energy-Aware Data Centers," Energies, MDPI, vol. 11(8), pages 1-17, August.
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    Cited by:

    1. Yibrah Gebreyesus & Damian Dalton & Sebastian Nixon & Davide De Chiara & Marta Chinnici, 2023. "Machine Learning for Data Center Optimizations: Feature Selection Using Shapley Additive exPlanation (SHAP)," Future Internet, MDPI, vol. 15(3), pages 1-17, February.

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