• Sunayana K. Guthale
• Sanjana B. Desale
• Abhishek V. Doltade
• Ravina S. Bachhav
• Namrata Ghuse

Thermal prediction for efficient energy management of clouds using machine learning involves using predictive models to optimize the cooling and energy consumption of cloud data centers. Cloud data centers require substantial cooling to maintain optimal operating temperatures for servers and other hardware. By accurately predicting the thermal behavior of the data center, energy consumption can be optimized, resulting in cost savings and improved overall efficiency. 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. Energy-efficient Cloud Infrastructure Allocation Of resources Framework is gaining recognition because it focuses on cloud data management to increase profit while minimizing costs. For efficient resource management, accurate host temperature prediction is essential. In this paper we proposed a method for prediction of thermal energy and scheduling it properly.

Cloud computing, machine learning, Resource Management System (RMS), Reinforcement Learning, Linear Regression, Green Cloud, Gradient Boosting (XGBoost).

"Thermal Prediction using Machine Learning for Efficient Energy Management of Cloud ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.l244-l248, May-2023, Available :http://www.jetir.org/papers/JETIR2305B35.pdf

"Thermal Prediction using Machine Learning for Efficient Energy Management of Cloud ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppl244-l248, May-2023, Available at : http://www.jetir.org/papers/JETIR2305B35.pdf