The exponential growth in data generation and processing has led to a significant increase in energy consumption in data centers. This has raised concerns regarding the environmental impact and the economic viability of these facilities. This paper presents a novel approach to optimize energy efficiency in data centers through the application of machine learning techniques. The study focuses on the development of a predictive model that can forecast energy demand based on historical data and real-time operational parameters. By analyzing trends and patterns in data center operations, the model can suggest adaptive strategies that minimize energy consumption without compromising performance. The proposed machine learning algorithms are trained on a dataset that includes various metrics such as server utilization, cooling system performance, and network traffic. The results demonstrate a significant reduction in energy consumption with minimal impact on the data center's performance. Furthermore, the study evaluates the robustness of the model under different operational conditions and highlights the potential for scalability and integration into existing data center infrastructures. The findings suggest that machine learning can play a crucial role in achieving sustainable energy practices in the fast-evolving field of data center management.
Jackson, E. Optimization of Energy Efficiency in Data Centers Using Machine Learning. Transactions on Engineering and Technology, 2023, 5, 37. https://doi.org/10.69610/j.tet.20230313
AMA Style
Jackson E. Optimization of Energy Efficiency in Data Centers Using Machine Learning. Transactions on Engineering and Technology; 2023, 5(1):37. https://doi.org/10.69610/j.tet.20230313
Chicago/Turabian Style
Jackson, Emma 2023. "Optimization of Energy Efficiency in Data Centers Using Machine Learning" Transactions on Engineering and Technology 5, no.1:37. https://doi.org/10.69610/j.tet.20230313
APA style
Jackson, E. (2023). Optimization of Energy Efficiency in Data Centers Using Machine Learning. Transactions on Engineering and Technology, 5(1), 37. https://doi.org/10.69610/j.tet.20230313
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