Thermal Prediction for Efficient Energy Management of Clouds using Machine Learning
This addresses cooling costs and reliability issues for cloud data center operators, but it is incremental as it applies existing machine learning methods to a specific domain problem.
The paper tackles thermal prediction in cloud data centers by proposing a gradient boosting model that predicts host temperature with an average RMSE of 0.05 (2.38°C error), 6°C less than an existing model, and a dynamic scheduling algorithm that reduces peak temperature by 6.5°C and energy consumption by 34.5% compared to a baseline.
Thermal management in the hyper-scale cloud data centers is a critical problem. Increased host temperature creates hotspots which significantly increases cooling cost and affects reliability. Accurate prediction of host temperature is crucial for managing the resources effectively. Temperature estimation is a non-trivial problem due to thermal variations in the data center. Existing solutions for temperature estimation are inefficient due to their computational complexity and lack of accurate prediction. However, data-driven machine learning methods for temperature prediction is a promising approach. In this regard, we collect and study data from a private cloud and show the presence of thermal variations. We investigate several machine learning models to accurately predict the host temperature. Specifically, we propose a gradient boosting machine learning model for temperature prediction. The experiment results show that our model accurately predicts the temperature with the average RMSE value of 0.05 or an average prediction error of 2.38 degree Celsius, which is 6 degree Celsius less as compared to an existing theoretical model. In addition, we propose a dynamic scheduling algorithm to minimize the peak temperature of hosts. The results show that our algorithm reduces the peak temperature by 6.5 degree Celsius and consumes 34.5% less energy as compared to the baseline algorithm.