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What is Thermal Prediction in Cloud Management about?

The document describes a machine learning approach for thermal prediction and efficient energy management in cloud data centers. It discusses an XGBoost algorithm to accurately predict host temperature with average error of 2.38°C. A dynamic scheduling algorithm using these predictions reduces peak temperature by 6.5°C and energy consumption by 34.5% compared to baseline. The system architecture collects data, trains and validates a predictive model for runtime thermal management and resource optimization i

Author

Shashank K G

Language

EN