Abstract
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.
Highlights
Information and communication technology (ICT) systems have become an important tool for supporting the infrastructure of social life
We found that the value when other explanatory variables were excluded was not significantly different from the case where all the explanatory variables were used
We found that explanatory variables such as rack position, rack intake temperature one minute before stopping, and CRAC2 cooling capacity one minute before stopping are important in this verification case
Summary
Information and communication technology (ICT) systems have become an important tool for supporting the infrastructure of social life. The role of data centers (DCs) for managing information is becoming increasingly important [1]. The development of cloud computing, the virtualization of communication technology (ICT), the tendency for high heat density of ICT, and the variety of cooling methods has led to complicated environments in which various factors must be considered [2,3]. Even in such complicated environments, more reliable DC operation is required. When temperature management is not done properly, hot spots may occur, resulting in poor service quality and service interruption
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