Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11): 199-207.doi: 10.11896/jsjkx.200900009

• Database & Big Data & Data Science • Previous Articles     Next Articles

Data Placement Strategy of Scientific Workflow Based on Fuzzy Theory in Hybrid Cloud

LIU Zhang-hui1,2, ZHAO Xu1,2, LIN Bing2,3, CHEN Xing1,2   

  1. 1 College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350116,China
    2 Fujian Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou 350116,China
    3 College of Physics and Energy,Fujian Normal University,Fuzhou 350117,China
  • Received:2020-09-01 Revised:2020-12-06 Online:2021-11-15 Published:2021-11-10
  • About author:LIU Zhang-hui,born in 1971,master,associate professor,postgraduate supervisor,is a member of China Computer Federation.His main research interests include big data technology and intelligent computing.
    LIN Bing,born in 1986,Ph.D,lecturer,postgraduate supervisor,is a member of China Computer Federation.His main research interests include cloud computing and intelligent computing and its application.
  • Supported by:
    National Key R & D Program of China(2018YFB1004800) and Guiding Project of Fujian Province(2018H0017).

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Abstract: A reasonable data placement strategy is essential to the efficient execution of scientific workflow in hybrid cloud environment.The traditional data placement strategy mainly focuses on the deterministic environment,but the data transmission time is uncertain due to the different load,bandwidth fluctuation and network congestion between different data centers and computer characteristics in the actual network environment.To solve this problem,a fuzzy adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operator (FGA-DPSO) is proposed to minimize the fuzzy transmission time of data,place the scientific workflow data reasonably and meet the privacy requirements of the data set and the capacity limit of the data center.The experimental results show that the algorithm can effectively reduce the fuzzy data transmission time of scientific workflow in hybrid cloud environment.

Key words: Data placement, Fuzzy theory, Hybrid cloud, Scientific workflow, Time optimization

CLC Number: 

  • TP338
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