Power saving based on characteristics of machine learning in data center

被引：0
作者：
机构：
[1] School of Computer Science, Fudan University
来源：
Wang, Z.-G. (zgwang@fudan.edu.cn) | 1600年 / Chinese Academy of Sciences卷 / 25期
关键词：
Distributed computing; Machine learning; MapReduce; PageRank; Power saving;
D O I：
10.13328/j.cnki.jos.004601
中图分类号：
学科分类号：
摘要：
With the development of the Internet, the scale of data center increases dramatically. How to analyze the data stored in the data center becomes the hot research topic. Programmers resort to the machine learning to analyze unstructured or semi-structured data. Thus, energy efficient machine learning is crucial for green data centers. Based the observation that there is redundant computation in the machine learning applications, this paper proposes a system which can save the power usage by removing the redundant computations and reusing the previous computation results. Evalution shows that for the typical k-means and PageRank applications the presented algorithm results 23% and 17% power saving. © 2014 ISCAS.
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页码：1432 / 1447
页数：15
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