Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory
The smart factory of Industry 4.0 has been regarded as a solution for handling the increasing production complexity caused by growing global economy and demand for customized products. Besides, it will make the interactions between humans, machines, and products become a highly competitive area for...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8165962/ |
| _version_ | 1818557142406266880 |
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| author | Boyu Li Zhipeng Zhao Yan Guan Ning Ai Xiaowen Dong Bin Wu |
| author_facet | Boyu Li Zhipeng Zhao Yan Guan Ning Ai Xiaowen Dong Bin Wu |
| author_sort | Boyu Li |
| collection | DOAJ |
| description | The smart factory of Industry 4.0 has been regarded as a solution for handling the increasing production complexity caused by growing global economy and demand for customized products. Besides, it will make the interactions between humans, machines, and products become a highly competitive area for market capitalization in the near feature. Nowadays, cloud computing with the high performance of computing and self-service access plays an important role in realizing smart factor. To minimize the overall cost of company in a heterogeneous cloud environment, including multiple public clouds, while ensuring a proper level of quality-of-service, task placement across multiple public clouds is a critical problem, where task deadlines and long-haul data transmission costs between smart factory and different clouds must be considered. We formulate this task placement problem as an integer linear program (ILP) to minimize company cost under the task deadline constraint. With extensive simulations, we evaluate the performance of our proposed ILP model in heterogeneous public clouds with finite and infinite resources. |
| first_indexed | 2024-12-13T23:56:07Z |
| format | Article |
| id | doaj.art-f12905d990fe4048b81ad8f48ae519fa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T23:56:07Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f12905d990fe4048b81ad8f48ae519fa2022-12-21T23:26:33ZengIEEEIEEE Access2169-35362018-01-0161560156410.1109/ACCESS.2017.27794628165962Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart FactoryBoyu Li0https://orcid.org/0000-0001-7015-3764Zhipeng Zhao1Yan Guan2Ning Ai3Xiaowen Dong4Bin Wu5School of Computer Science and Technology, Tianjin University, Tianjin, ChinaSchool of Computer Science and Technology, Tianjin University, Tianjin, ChinaSchool of Computer Science and Technology, Tianjin University, Tianjin, ChinaSchool of Computer Science and Technology, Tianjin University, Tianjin, ChinaDC Technology Laboratory, Huawei Technologies Company Ltd., Shenzhen, ChinaSchool of Computer Science and Technology, Tianjin University, Tianjin, ChinaThe smart factory of Industry 4.0 has been regarded as a solution for handling the increasing production complexity caused by growing global economy and demand for customized products. Besides, it will make the interactions between humans, machines, and products become a highly competitive area for market capitalization in the near feature. Nowadays, cloud computing with the high performance of computing and self-service access plays an important role in realizing smart factor. To minimize the overall cost of company in a heterogeneous cloud environment, including multiple public clouds, while ensuring a proper level of quality-of-service, task placement across multiple public clouds is a critical problem, where task deadlines and long-haul data transmission costs between smart factory and different clouds must be considered. We formulate this task placement problem as an integer linear program (ILP) to minimize company cost under the task deadline constraint. With extensive simulations, we evaluate the performance of our proposed ILP model in heterogeneous public clouds with finite and infinite resources.https://ieeexplore.ieee.org/document/8165962/Cloud computingheterogeneous cloudintegral linear programming (ILP)task placement |
| spellingShingle | Boyu Li Zhipeng Zhao Yan Guan Ning Ai Xiaowen Dong Bin Wu Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory IEEE Access Cloud computing heterogeneous cloud integral linear programming (ILP) task placement |
| title | Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory |
| title_full | Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory |
| title_fullStr | Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory |
| title_full_unstemmed | Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory |
| title_short | Task Placement Across Multiple Public Clouds With Deadline Constraints for Smart Factory |
| title_sort | task placement across multiple public clouds with deadline constraints for smart factory |
| topic | Cloud computing heterogeneous cloud integral linear programming (ILP) task placement |
| url | https://ieeexplore.ieee.org/document/8165962/ |
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