  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.
