A Deep Learning-Based Thermal Prediction Approach for Energy Management in Cloud Data Centers

Escalating host temperatures in data centers can create hot spots, significantly boosting cooling costs and impacting reliability. Accurately predicting host temperatures is of the utmost importance in managing resources effectively. Existing temperature estimation solutions are inefficient due to a lack of accurate prediction. To this end, we proposed a deep learning-based thermal prediction approach called DLTPA, which aims to minimize the temperature and power consumption of virtual machines (VMs) in data centers. Specifically, we built a Long Short-Term Memory (LSTM) network model to accurately predict host temperature and power consumption, demonstrating superior performance over traditional algorithms. Our LSTM model exhibits exceptional accuracy in predicting thermal and energy dynamics, achieving an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.98 in power consumption prediction, indicating exact forecasts. Furthermore, we design an efficient VM placement strategy to achieve host peak temperature reduction by rationally arranging VM tasks. The experimental results demonstrate that the DLTPA significantly improves over other leading-edge algorithms. It reduces peak power consumption by 3.64% to 9.39%, lowers average temperature by 3.21% to 7.96%, achieves a 0% SLA violation rate, and maintains a high level of load balancing at 19.8%.