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Neural Network Modeling in Model-Based Control of a Data Center

[+] Author Affiliations
Nachiket Kansara, Rohit Katti, Alan P. Bowling

University of Texas at Arlington, Arlington, TX

Kourosh Nemati, Bahgat Sammakia

Binghamton University-SUNY, Binghamton, NY

Paper No. IPACK2015-48684, pp. V001T09A034; 6 pages
  • ASME 2015 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems collocated with the ASME 2015 13th International Conference on Nanochannels, Microchannels, and Minichannels
  • Volume 1: Thermal Management
  • San Francisco, California, USA, July 6–9, 2015
  • Conference Sponsors: Electronic and Photonic Packaging Division
  • ISBN: 978-0-7918-5688-8
  • Copyright © 2015 by ASME


This paper presents the development of a neural network model of the server temperature to be used in model-based control of a data center. Data centers provide the optimal environments for operation of servers and storage devices. Conventionally, computational fluid dynamics (CFD) has been used to model the dynamic and complex environment of the data center. However, the drawback of this approach is its computational inefficiency. The effects of changing a single input may take an entire day to compute. Thus the CFD model is not well suited for model-based control. Instead, we propose to use an artificial Neural Network (NN) model which predicts server temperatures in significantly less time. In addition, this NN model has the capability of learning the environment in the data center by adapting its parameters in real time based on sensor data continuously taken from the data center. This work discusses the current development of the neural network, work being done at the University of Texas at Arlington, to include modeling of transient conditions, or time related changes, using data generated in a test bed Data Center at SUNY Binghamton.

Copyright © 2015 by ASME



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