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 $^\circ \mathrm{C}$∘C, which is 6 $^\circ \mathrm{C}$∘C 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 $^\circ \mathrm{C}$∘C and consumes 34.5 percent less energy as compared to the baseline algorithm.

中文翻译：

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使用机器学习对云进行高效能源管理的热预测

超大规模云数据中心的热管理是一个关键问题。主机温度升高会产生热点，这会显着增加冷却成本并影响可靠性。准确预测宿主温度对于有效管理资源至关重要。由于数据中心的热变化，温度估计是一个重要的问题。由于计算复杂且缺乏准确预测，现有的温度估计解决方案效率低下。然而，用于温度预测的数据驱动机器学习方法是一种很有前途的方法。在这方面，我们从私有云收集和研究数据，并显示热变化的存在。我们研究了几种机器学习模型来准确预测宿主温度。具体来说，我们提出了一种用于温度预测的梯度提升机器学习模型。实验结果表明，我们的模型准确预测了温度，平均RMSE值为0.05，平均预测误差为2.38$^\circ \mathrm{C}$∘C，这是 6 $^\circ \mathrm{C}$∘C与现有的理论模型相比。此外，我们提出了一种动态调度算法来最小化主机的峰值温度。结果表明，我们的算法将峰值温度降低了 6.5$^\circ \mathrm{C}$∘C 与基线算法相比，能耗降低了 34.5%。