Dynamic Resource Provisioning With Fault Tolerance for Data-Intensive Meteorological Workflows in Cloud
Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes fo...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2020-09, Vol.16 (9), p.6172-6181 |
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| creator | Xu, Xiaolong Mo, Ruichao Dai, Fei Lin, Wenmin Wan, Shaohua Dou, Wanchun |
| description | Cloud computing is a formidable paradigm to provide resources for handling the services from Industrial Internet of Things (IIoT), such as meteorological industry. Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. Finally, comprehensive experimental analysis of DRPM are proceeded. |
| doi_str_mv | 10.1109/TII.2019.2959258 |
| format | Article |
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Generally, the meteorological services, with complex interdependent logics, are modeled as workflows. When any of the computing nodes for hosting the meteorological workflows fail, all sorts of consequences (e.g., data loss, makespan enlargement, performance degradation, etc.) could arise. Thus recovering the failed tasks as well as optimizing the makespan and the load balance of the computing nodes is still a critical challenge. To address this challenge, a dynamic resource provisioning method (DRPM) with fault tolerance for the data-intensive meteorological workflows is proposed in this article. Technically, the Virtual Layer 2 (VL2) network topology is exploited to build meteorological cloud infrastructure. Then, the nondominated sorting genetic algorithm II (NSGA-II) is employed to minimize the makespan and improve the load balance. 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| subjects | Cloud computing Computational modeling Data loss Dynamic scheduling Enlargement Fault tolerance Fault tolerant systems Genetic algorithms Industrial applications Internet of Things Load balancing Meteorological services meteorological workflow Mirrors Network topologies Nodes nondominated sorting genetic algorithm (NSGA)-II Performance degradation Provisioning Resource allocation Sorting algorithms Task analysis |
| title | Dynamic Resource Provisioning With Fault Tolerance for Data-Intensive Meteorological Workflows in Cloud |
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