TY - GEN
T1 - Using machine learning for data center cooling infrastructure efficiency prediction
AU - Shoukourian, Hayk
AU - Wilde, Torsten
AU - Labrenz, Detlef
AU - Bode, Arndt
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - 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.
AB - 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.
KW - COP
KW - data center
KW - energy efficiency
KW - high performance computing
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85028046079&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2017.25
DO - 10.1109/IPDPSW.2017.25
M3 - Conference contribution
AN - SCOPUS:85028046079
T3 - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
SP - 954
EP - 963
BT - Proceedings - 2017 IEEE 31st International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2017
Y2 - 29 May 2017 through 2 June 2017
ER -
