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Online Updating of Computer Model Output Using Real-Time Sensor Data

Summary: [This abstract is based on the authors' abstract.] Data center thermal management has become increasingly important because of massive computational demand in information technology. To advance the understanding of the thermal environment in a data center, complex computer models are extensively used to simulate temperature distribution maps. However, due to management policies and time constraints, it is not practical to execute such models in a real time fashion. In this article, we propose a novel statistical modeling method to perform real-time simulation by dynamically fusing a base, steady-state solution of a computer model, and real-time thermal sensor data. The proposed method uses a Kalman filter and stochastic gradient descent method as computational tools to achieve real-time updating of the base temperature map. We evaluate the performance of the proposed method through a simulation study and demonstrate its merits in a data center thermal management application. Supplementary materials for this article are available online.

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  • Topics: Software and Technology (for statistics, measurement, analysis), Statistics
  • Keywords: Computer models, Temperature, Measurement, Space-time modeling, Parameters, Simulations
  • Author: Jiang, Huijing; Deng, Xinwei; López, Vanessa; Hamann, Hendrik F.
  • Journal: Technometrics