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[1] 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.[2]

Another example of the same technology was presented in Russia[3] 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

  1. ↑ IT World, 'Romonet brings predictive data center tool to US', June 28th, 2011
  2. ↑ Altaterra, 'Zen and the Art of Data Center Greening (and Energy Efficiency)', June 28th, 2011
  3. ↑ 'Load Prediction for HPC Energy Efficiency Improvement (in Russian)'

External links


This article is issued from Wikipedia - version of the Wednesday, June 25, 2014. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.