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Issue No. 04 - July-Aug. (2018 vol. 11)
ISSN: 1939-1374
pp: 727-739
Lizhen Cui , School of Computer Science and Technology, Shandong University, Jinan, Shandong, China
Junhua Zhang , School of Computer Science and Technology, Shandong University, Jinan, Shandong, China
Lingxi Yue , School of Computer Science and Technology, Shandong University, Jinan, Shandong, China
Yuliang Shi , School of Computer Science and Technology, Shandong University, Jinan, Shandong, China
Hui Li , School of Computer Science and Technology, Shandong University, Jinan, Shandong, China
Dong Yuan , School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, Australia
ABSTRACT
Cloud computing is a promising distributed computing platform for big data applications, e.g., scientific applications, since excessive resources can be obtained from cloud services for processing and storing both existing and generated application datasets. However, when tasks process big data stored in distributed data centers, the inevitable data movements will cause huge bandwidth cost and execution delay. In this paper, we construct a tripartite graph based model to formulate the data replica placement problem and propose a genetic algorithm based data replica placement strategy for scientific applications to reduce data transmissions in cloud. Our approach can reduce 1) the size of moved data, 2) the time of data movement and 3) the number of movements. We conduct experiments to compare the proposed strategy with the random placement strategy used in Hadoop Distributed Files System (HDFS), which demonstrates that our strategy has better performance for scientific applications in clouds.
INDEX TERMS
Genetic algorithms, Data communication, Distributed databases, Data models, Cloud computing, Bandwidth, Big data
CITATION

L. Cui, J. Zhang, L. Yue, Y. Shi, H. Li and D. Yuan, "A Genetic Algorithm Based Data Replica Placement Strategy for Scientific Applications in Clouds," in IEEE Transactions on Services Computing, vol. 11, no. 4, pp. 727-739, 2018.
doi:10.1109/TSC.2015.2481421
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