Rack Temperature Prediction Model Using Machine Learning after Stopping Computer Room Air Conditioner in Server Room

• Published in: Energies (Basel) 2020-09, Vol.13 (17), p.4300
• Main Authors: Sasakura, Kosuke, Aoki, Takeshi, Komatsu, Masayoshi, Watanabe, Takeshi
• Format: Article
• Language: English
• Subjects: Accuracy; Air conditioners; Air temperature; Calcium channels; Communications equipment; Computer centers; continuous and reliable operation; data center; Data centers; Experiments; Learning algorithms; Machine learning; Model accuracy; Prediction models; R&D; Research & development; Root-mean-square errors; Sensors; server room; Servers; Shutdowns; temperature environment; temperature prediction; Temperature requirements; Trends; Variables
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en13174300

Data centers (DCs) are becoming increasingly important in recent years, and highly efficient and reliable operation and management of DCs is now required. The generated heat density of the rack and information and communication technology (ICT) equipment is predicted to get higher in the future, so it is crucial to maintain the appropriate temperature environment in the server room where high heat is generated in order to ensure continuous service. It is especially important to predict changes of rack intake temperature in the server room when the computer room air conditioner (CRAC) is shut down, which can cause a rapid rise in temperature. However, it is quite difficult to predict the rack temperature accurately, which in turn makes it difficult to determine the impact on service in advance. In this research, we propose a model that predicts the rack intake temperature after the CRAC is shut down. Specifically, we use machine learning to construct a gradient boosting decision tree model with data from the CRAC, ICT equipment, and rack intake temperature. Experimental results demonstrate that the proposed method has a very high prediction accuracy: the coefficient of determination was 0.90 and the root mean square error (RMSE) was 0.54. Our model makes it possible to evaluate the impact on service and determine if action to maintain the temperature environment is required. We also clarify the effect of explanatory variables and training data of the machine learning on the model accuracy.