TY - GEN
T1 - Modeling of HVAC system in data center with super-multipoint temperature sensing technology
AU - Wasa, Yasuaki
AU - Makihara, Fumiya
AU - Kato, Akihito
AU - Kasajima, Takeo
AU - Hatanaka, Takeshi
AU - Fujita, Masayuki
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/9
Y1 - 2014/12/9
N2 - In this paper, we consider a server cooling system in a data center with fans and a super-multipoint temperature sensing technology using optimal time-domain reflectometry of optical fiber. The sensing system was developed to visualize the temperature distribution of the room in real time, and it can also be a key technology to control the distribution in order to reduce total power consumption in data centers. In this paper, we first present a concept of the fan control system. The objective of the system is to uniformize the rack air inlet temperature distribution in the presence of heat from each server. Since the control scheme requires a dynamical model of the temperature variations in the room, this paper mainly addresses the modeling. The challenge of the modeling stems from a large volume of data provided by the sensing system. It is computationally hard to directly apply standard identification techniques to the data. We thus present a two-stage reduction scheme of the output dimension using the concept of so-called mutual information. The effectiveness of the proposed scheme is finally demonstrated using real data.
AB - In this paper, we consider a server cooling system in a data center with fans and a super-multipoint temperature sensing technology using optimal time-domain reflectometry of optical fiber. The sensing system was developed to visualize the temperature distribution of the room in real time, and it can also be a key technology to control the distribution in order to reduce total power consumption in data centers. In this paper, we first present a concept of the fan control system. The objective of the system is to uniformize the rack air inlet temperature distribution in the presence of heat from each server. Since the control scheme requires a dynamical model of the temperature variations in the room, this paper mainly addresses the modeling. The challenge of the modeling stems from a large volume of data provided by the sensing system. It is computationally hard to directly apply standard identification techniques to the data. We thus present a two-stage reduction scheme of the output dimension using the concept of so-called mutual information. The effectiveness of the proposed scheme is finally demonstrated using real data.
UR - http://www.scopus.com/inward/record.url?scp=84920545534&partnerID=8YFLogxK
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U2 - 10.1109/CCA.2014.6981363
DO - 10.1109/CCA.2014.6981363
M3 - Conference contribution
AN - SCOPUS:84920545534
T3 - 2014 IEEE Conference on Control Applications, CCA. Part of 2014 IEEE Multi-conference on Systems and Control, MSC 2014
SP - 304
EP - 309
BT - 2014 IEEE Conference on Control Applications, CCA. Part of 2014 IEEE Multi-conference on Systems and Control, MSC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Conference on Control Applications, CCA 2014
Y2 - 8 October 2014 through 10 October 2014
ER -