A Sensor System for High-Fidelity Temperature Distribution Forecasting in Data Centers

Abstract

Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing server shutdowns because of overheating and improving a data center’s energy efficiency. This article presents a novel cyber-physical approach for temperature forecasting in data centers, one that integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach reduces not only the computational complexity of online temperature modeling and prediction, but also the number of deployed sensors, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of a monitored data center. We extensively evaluated the proposed system on a rack of 15 servers and a testbed of five racks and 229 servers in a small-scale production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52○C, within a duration up to 10 minutes. Moreover, our approach can reduce the required number of sensors by 67% while maintaining desirable prediction fidelity.