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

Xinwei Deng
Virginia Tech
Room 306, Statistics Building 1130

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.

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