Abstract
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.
Highlights
With the rapid increase in the scale and the number of the data centers, the data center energy consumption is growing rapidly
The data center energy management mainly uses DVS / DVFS or sleep / wake technology, which make the idle nodes with the low energy consumption state [2,3,4]
The energy saving for data center is mainly on Map Reduce computing tasks. These algorithms can reduce the energy consumption on certain extent, but these algorithms are subject to certain restrictions which did not take account of the typical application running in the data center
Summary
With the rapid increase in the scale and the number of the data centers, the data center energy consumption is growing rapidly. The energy saving for data center is mainly on Map Reduce computing tasks. These algorithms can reduce the energy consumption on certain extent, but these algorithms are subject to certain restrictions which did not take account of the typical application (machine learning task) running in the data center. We analyze the typical machine learning algorithms to saving the energy consumption of the data center. The MapReduce Computing Framework is a widely used as a programming model in the data center; there is a lot of work to focus on the study energy consumption and specific control methods of MapReduce model.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
