Online Updating of Computer Model Output Using Real-Time Sensor Data. Issue 4 (1st October 2016)

• Record Type: Journal Article
• Authors: Jiang, Huijing; Deng, Xinwei; López, Vanessa; Hamann, Hendrik F.
• Is Part Of: Technometrics. Volume 58:Issue 4(2016)
• Page Start: 472
• Page End: 482
• Publication Date: 2016-10-01
• Subjects: Data fusion -- Dynamic Gaussian process -- Kalman filter -- Online optimization -- Stochastic projected gradient.; Statistical physics -- Periodicals; Chemistry -- Statistical methods -- Periodicals; Engineering -- Statistical methods -- Periodicals; 519.5
• DOI: 10.1080/00401706.2015.1080761
• Languages: English
• ISSNs: 0040-1706

Abstract

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.