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| DCPM - Data Center Predictive Modeling ： ウィキペディア英語版 |
Data center predictive modeling
Data center predictive modeling (DCPM) is the ability to forecast the performance of a data center into the future, be it its energy use, energy efficiency, performance of the myriad pieces of equipment, even cost.
An important part of forecasting data center performance is the use of computational fluid dynamics (CFD) to quantify the airflow and temperatures that would occur if physical changes were made to the data center space. The use of CFD moves DCPM from a probabilistic type of forecasting to a physics-based one.
The term DCPM has been in use since June 2011〔(IT World, 'Romonet brings predictive data center tool to US', June 28th, 2011 )〕 and was adopted by Romonet to differentiate DCPM from data center infrastructure management (DCIM) which only tracks the present performance of the elements of a data center.〔(Altaterra, 'Zen and the Art of Data Center Greening (and Energy Efficiency)', June 28th, 2011 )〕
Another example of the same technology was presented in Russia〔('Load Prediction for HPC Energy Efficiency Improvement (in Russian)' )〕 by Institute of Applied Mathematical Research, Karelian Research Centre, Russian Academy of Sciences. The technology is developed since 2011 under support of FASIE and RFBR.
==References==
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
■ウィキペディアで「Data center predictive modeling」の詳細全文を読む
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