Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments
In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data tran...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2020/8105145 |
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| _version_ | 1832566510602682368 |
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| author | Zheyi Chen Xu Zhao Bing Lin |
| author_facet | Zheyi Chen Xu Zhao Bing Lin |
| author_sort | Zheyi Chen |
| collection | DOAJ |
| description | In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments. |
| format | Article |
| id | doaj-art-dfab38d45b3349ca8c76e2646e35e633 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-dfab38d45b3349ca8c76e2646e35e6332025-02-03T01:04:01ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2020-01-01202010.1155/2020/81051458105145Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud EnvironmentsZheyi Chen0Xu Zhao1Bing Lin2College of Engineering Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UKFujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou 350118, ChinaCollege of Physics and Energy, Fujian Normal University, Fuzhou 350117, ChinaIn hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.http://dx.doi.org/10.1155/2020/8105145 |
| spellingShingle | Zheyi Chen Xu Zhao Bing Lin Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments Discrete Dynamics in Nature and Society |
| title | Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments |
| title_full | Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments |
| title_fullStr | Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments |
| title_full_unstemmed | Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments |
| title_short | Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments |
| title_sort | fuzzy theory based data placement for scientific workflows in hybrid cloud environments |
| url | http://dx.doi.org/10.1155/2020/8105145 |
| work_keys_str_mv | AT zheyichen fuzzytheorybaseddataplacementforscientificworkflowsinhybridcloudenvironments AT xuzhao fuzzytheorybaseddataplacementforscientificworkflowsinhybridcloudenvironments AT binglin fuzzytheorybaseddataplacementforscientificworkflowsinhybridcloudenvironments |