A machine learning solution for data center thermal characteristics analysis - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Energies Année : 2020

A machine learning solution for data center thermal characteristics analysis

Résumé

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.
Fichier principal
Vignette du fichier
energies-13-04378.pdf (1.81 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-02922059 , version 1 (25-10-2023)

Identifiants

Citer

Anastasiia Grishina, Marta Chinnici, Ah-Lian Kor, Eric Rondeau, Jean-Philippe Georges. A machine learning solution for data center thermal characteristics analysis. Energies, 2020, 13 (17), pp.4378. ⟨10.3390/en13174378⟩. ⟨hal-02922059⟩
67 Consultations
6 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More