ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)
|Session 3: Computer
|05 September 2017
Energy Efficiency Optimizing Based on Characteristics of Machine Learning in Cloud Computing
1 School of basic and Information Engineering, Yunnan Agricultural University, Kunming, China
2 Yunnan Agricultural University library, Kunming, China
3 School of Electrical Engineering, Kunming Metallurgy College, Kunming, China
Energy efficiency is one of the most important issues for large-scale server systems in current cloud computing. the main method about the power-performance tradeoff by fixing one factor and minimizing the other, from the perspective of optimal load distribution. However, there still exist several main challenges about Energy efficiency due to the complexities of real cloud computing application scene. The paper adopts machine learning theory to save energy consumption by decrease redundant computation for high energy-efficiency cloud computing environment. give the typical k-means and Page Rank applications, the Experiments show that the presented algorithm can save power consumption apparently. The research combines the machine learning theory and distributed technology, and presents a creative way to challenged problems in energy-efficiency cloud.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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