A High-Fidelity Temperature Distribution Forecasting System for Data Centers
Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing over heating induced server shutdowns and improving a data center's energy efficiency. This paper presents a novel cyber-physical approach for temperature forecasting in data centers, which integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure the forecasting fidelity, the authors leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback.