Using Machine Learning for Data Center Cooling Infrastructure Efficiency Prediction

Abstract

Power consumption continues to remain a critical aspect for High Performance Computing (HPC) data centers. It becomes even more crucial for Exascale computing since scaling today's fastest system to an Exaflop level would consume more than 168 MW power which is 8 times higher than the 20 MW power consumption goal set, at the time of this publication, by the US Department of Energy. This naturally leads to a necessity for energy efficiency improvement that will encompass the full chain of the power consumers, starting from the data center infrastructure, including cooling overheads and electrical losses, up to compute resource scheduling and application scaling. In this paper a machine learning approach is proposed to model the Coefficient of Performance (COP) of HPC data center's hot water cooling loop. The suggested model is validated on operational data obtained at Leibniz Supercomputing Centre (LRZ). The paper shows how this COP model can help to improve the energy efficiency of modern HPC data centers.