Thermal Prediction for Immersion Cooling Data Centers Based on Recurrent Neural Networks

Abstract

In the data center’s scope, current cooling techniques are not very efficient both in terms of energy, consuming up to 40% of the total energy requirements, and in terms of occupied area. This is a critical problem for the development of new smart cities, which require the proliferation of numerous data centers in urban areas, to reduce latency and bandwidth of processing data analytics applications in real time. In this work, we propose a new disruptive solution developed to address this problem, submerging the computing infrastructure in a tank full of a dielectric liquid based on hydro-fluoro-ethers (HFE). Thus, we obtain a passive two phase-cooling system, achieving zero-energy cooling and reducing its area. However, to ensure the maximum heat transfer capacity of the HFE, it is necessary to ensure specific thermal conditions. Making a predictive model is crucial for any system that needs to work around the point of maximum efficiency. Therefore, this research focuses on the implementation of a predictive thermal model, accurate enough to keep the temperature of the cooling system within the maximum efficiency region, under real workload conditions. In this paper, we successfully obtained a predictive thermal model using a neural network architecture based on a Gated Recurrent Unit. This model makes accurate thermal predictions of a real system based on HFE immersion cooling, presenting an average error of 0.75 C with a prediction window of 1 min.