A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud
A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud
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DOI：
10.1002/cpe.4844
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发表时间：
2018-08
期刊：
影响因子：
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通讯作者：
Siguang Li;Zhengwen Huang;Liangxiu Han;Changjun Jiang
中科院分区：
文献类型：
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作者：
Siguang Li;Zhengwen Huang;Liangxiu Han;Changjun Jiang
过去几年见证了云计算基础设施的快速部署，以支持数据密集型应用程序。现有的工作主要集中在数据重用机制上，没有考虑数据处理路由，这在云中计算节点之间交换数据时，会显著影响计算成本。本文提出了一种基于遗传算法的自动数据流管理解决方案（ADFMS），该方案实现了自动路由功能和自调节的中间数据管理机制，以实现高效的云计算数据处理结构。实验结果表明，ADFMS优化了云中中间数据的管理成本。
The past few years have witnessed a rapid deployment of computing infrastructures in the cloud in support of data intensive applications. The effort of the existing works is mainly focused on data reusing mechanisms without considering data processing routes, which can significantly affect the computation costs when exchanging data among the computing node in the cloud. This paper presents a genetic algorithm enhanced Automatic Data Flow Management Solution (ADFMS) that facilitates automatic routing function and a self‐adjustable intermediate data management mechanism to achieve an efficient data processing structure of cloud computing. Experimental results show that ADFMS optimizes costs in managing intermediate data in the cloud.