Using Machine Learning to Improve Energy Efficiency in Service Provider Networks with Novel Traffic Prediction

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A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks is a research paper published by MDPI, an Open Access publisher of peer-reviewed scientific articles. This paper proposes a new traffic prediction system that uses machine learning techniques to improve energy efficiency within service provider networks. The authors, Zhang et. al, experimented with different machine learning models such as Support Vector Machine regression (SVM), Bayesian Network (BN) and Neural Network (NN). The results showed that the proposed model outperforms existing traffic prediction systems and increases energy efficiency of service provider networks.

MDPI, founded in 1996, is an Open Access publisher releasing scientific articles under the Creative Common CC BY license. MDPI is committed to providing authors and their work with top visibility and quality peer review. The company is one of the largest open access publishers in the world, with over 325 journals in topics including science, medicine, engineering, technology, and social sciences.

Lead author of the paper, Qian Zhang is an Associate Professor of Communication Engineering at Zhengzhou University, China. Zhang is also the Executive Director of the Data Communication Laboratory at the university. Over the course of his career, he has received several awards and scholarships for the scientific research. His research interests include advanced communication networks, traffic prediction, and energy efficiency.

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