A genetic algorithm enhanced automatic data flow management solution for facilitating data intensive applications in the cloud. (14th August 2018)

• Record Type: Journal Article
• Authors: Li, Siguang; Huang, Zhengwen; Han, Liangxiu; Jiang, Changjun
• Other Names: Barbosa Jorge G. guestEditor.; Jeannot Emmanuel guestEditor.; Li Maozhen guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 23(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-08-14
• Subjects: cloud computing -- computation cost optimization -- data flows -- genetic algorithm -- Petri Net; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4844
• Languages: English
• ISSNs: 1532-0626

Abstract

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.