Machine Learning based Thermal Prediction for Energy-efficient Cloud Computing

Energy-efficient workload management has been widely adopted by data centers for cloud computing. Thermal and energy modeling plays an important role in making decisions on workload management. Machine learning technology has become increasingly popular used in thermal modeling. In this paper, we studied existing machine learning algorithms and methods for thermal prediction for data centers and conducted experiments to investigate the impact of activities on the temperature and energy consumption with CPU-intensive workload. We collected the CPU utilization, temperature, and energy data, and applied several regression models and the XGBoost machine learning model to predict the temperature of the CPU. Performance of the regression models was compared with the XGBoost machine learning models. With more experiments are been conducting to investigate the CPU temperature under various combinations of CPU core utilizations, we will further improve the performance of the XGBoost machine learning model.