Sensor-based fast thermal evaluation model for energy efficient high-performance datacenters

In this work, we propose an abstract heat flow model which uses temperature information from onboard and ambient sensors, characterizes hot air recirculation based on these information, and accelerates the thermal evaluation process for high performance datacenters. This is critical to minimize energy costs, optimize computing resources, and maximize computation capability of the datacenters. Given a workload and thermal profile, obtained from various distributed sensors, we predict the resulting temperature distribution in a fast and accurate manner taking into account the recirculation characterization of a datacenter topology. Simulation results confirm our hypothesis that heat recirculation can be characterized as cross interference in our abstract heat flow model. Moreover, fast thermal evaluation based on cross interference can be used in online thermal management to predict temperature distribution in real-time.