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A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud
Author(s) -
Li Siguang,
Huang Zhengwen,
Han Liangxiu,
Jiang Changjun
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4844
Subject(s) - cloud computing , computer science , software deployment , distributed computing , computation , data flow diagram , data processing , genetic algorithm , data management , algorithm , data mining , database , operating system , machine learning
Summary 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.