Multi-scale collaborative modeling and deep learning-based thermal prediction for air-cooled data centers: An innovative insight for thermal management

Investigating the data center (DC) thermal environment and temperature distribution is crucial to responding to unforeseen events such as equipment failure or environmental changes. However, building full-scale simulation models from DC room level to chip level faces significant challenges. In this paper, we propose a distinctive approach that combines multi-scale collaborative modeling with deep learning techniques for thermal prediction in air-cooled DCs. By taking the simulation results of the parent model as the boundary conditions of the child model, we constructed the DC multi-scale simulation model, which significantly reduces the model complexity and computational resources. Leveraging experimental data, the models at different scales were validated separately. The effects of different cooling strategies, air supply temperatures and air supply flow rates on multi-scale simulation models were investigated. Based on the parametric simulation approach, datasets for training data-driven models are constructed. Simultaneously, we propose the CNN-BiLSTM-Attention neural network model to predict the maximum CPU temperature and optimize the hyperparameters of the neural network through by Bayesian optimization. The prediction results of the coupled multi-scale model and the deep learning prediction model show that the absolute error is controlled within ±0.1 K, and the R2 value of the model evaluation metric is as high as 0.9899. Herein, the results provide valuable insights for enhancing thermal management in air-cooled DCs, paving the way for more efficient and resilient DC operations in the future.