Energy consumption of data center has increased dramatically due to the massive computing demands driven from every sector of the economy. Hence, data center energy management has become very important for operating data centers within environmental standards while achieving low energy cost. In order to advance the understanding of thermal management in data centers, relevant environmental information such as temperature, humidity and air quality are gathered through a network of real-time sensors or simulated via sophisticated physical models (e.g. computational fluid dynamics models). However, sensor readings of environmental parameters are collected only at sparse locations and thus cannot provide a detailed map of temperature distribution for the entire data center. While the physics models yield high resolution temperature maps, it is often not feasible, due to computational complexity of these models, to run them in real-time, which is ideally required for optimum data center operation and management. In this work, we propose a novel statistical modeling approach to updating physical model outputs in real-time and providing automatic scheduling for re-computing physical model outputs. The proposed method dynamically corrects the discrepancy between a steady-state output of the physical model and real-time thermal sensor data. We show that the proposed method can provide valuable information for data center energy management such as real-time high-resolution thermal maps. Moreover, it can efficiently detect systematic changes in a data center thermal environment, and automatically schedule physical models to be re-executed whenever significant changes are detected.

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