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Rack Temperature Prediction Model Using Machine Learning after Stopping Computer Room Air Conditioner in Server Room
- Source :
- Energies, Volume 13, Issue 17, Energies, Vol 13, Iss 4300, p 4300 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
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.
- Subjects :
- Service (systems architecture)
Control and Optimization
Computer science
Energy Engineering and Power Technology
Machine learning
computer.software_genre
data center
lcsh:Technology
Rack
Heat density
Electrical and Electronic Engineering
Server room
Engineering (miscellaneous)
lcsh:T
Renewable Energy, Sustainability and the Environment
business.industry
server room
Construct (python library)
machine learning
continuous and reliable operation
Room air distribution
temperature environment
Artificial intelligence
business
temperature prediction
computer
Energy (miscellaneous)
Subjects
Details
- Language :
- English
- ISSN :
- 19961073
- Database :
- OpenAIRE
- Journal :
- Energies
- Accession number :
- edsair.doi.dedup.....bd90b4c23efdc64ca5c2b7776d52cae8
- Full Text :
- https://doi.org/10.3390/en13174300