Carbon-aware distributed cloud: multi-level grouping genetic algorithm
Carbon-aware distributed cloud: multi-level grouping genetic algorithm
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DOI：
10.1007/s10586-014-0359-y
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发表时间：
2015-03
期刊：
影响因子：
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通讯作者：
F. F. Moghaddam-F.;R. F. Moghaddam;M. Cheriet
中科院分区：
文献类型：
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作者：
F. F. Moghaddam-F.;R. F. Moghaddam;M. Cheriet
温室气体（GHG）排放引起的全球变暖是发达国家和发展中国家关注的主要问题之一。在快速发展的信息和通信技术产业中，现有的能源效率方法不足以解决复杂分布式系统优化等新问题。因此，为这类系统量身定制的适当方法可以显著减少其温室气体排放。本文提出了一种新的遗传算法，即多级分组遗传算法（MLGGA），该算法是针对数据中心网络上分布式云环境中碳足迹减少等多级装箱问题而设计的。在仿真平台上对该算法进行了实际数据测试，并将结果与其他最新方法进行了比较。结果表明，该算法的性能得到了显著提高。
Global warming caused by greenhouse gas (GHG) emissions is one of the main concerns for both developed and developing countries. In a fast growing Information and Communication Technology industry, current energy efficiency methodologies are not sufficient for new raising problems such as optimization of complex distributed systems. Therefore, proper methodologies tailored for this type of systems could significantly reduce their GHG emissions. In this paper, a new genetic algorithm (GA) is introduced, namely multi-level grouping GA (MLGGA), which is designed for multi-level bin packing problems such as that of carbon footprint reduction in a distributed cloud over a network of data centers. The new MLGGA algorithm is tested on real data in a simulation platform, and its results are compared with other state-of-the-art methodologies. The results show a significant increase in the performance achieved by the proposed algorithm.