A computing allocation strategy for Internet of things’ resources based on edge computing
In order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. First, based on the comprehensive consideration of...
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| Format: | Article |
| Language: | English |
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Wiley
2021-12-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211064800 |
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| _version_ | 1832553274936393728 |
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| author | Zengrong Zhang |
| author_facet | Zengrong Zhang |
| author_sort | Zengrong Zhang |
| collection | DOAJ |
| description | In order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. First, based on the comprehensive consideration of computing resources, bandwidth, and migration decisions, an optimization problem is constructed that minimizes the sum of all user task execution delays and energy consumption weights. Second, a dynamic offloading scheduling algorithm based on Q -learning is proposed based on the optimization problem. This algorithm makes full use of the computing power for cloud and edge, which effectively meets the demand for efficient computing services in Internet of Things’ scenarios. Finally, facing the environment dynamic changes of edge nodes in edge cloud, the algorithm can adaptively adjust the migration strategy. Experiments show that when the number of Internet of Things’ devices is 30, the total energy consumption of Internet of Things’ devices of proposed algorithm is reduced by 24.67% and 19.44%, respectively, compared with other algorithms. The experimental results show that proposed algorithm can effectively improve the success rate of task offloading and execution, which can reduce the local energy consumption. |
| format | Article |
| id | doaj-art-8a19fc0d507f4c2ab35cacc0e4a7edaf |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2021-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-8a19fc0d507f4c2ab35cacc0e4a7edaf2025-02-03T05:54:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772021-12-011710.1177/15501477211064800A computing allocation strategy for Internet of things’ resources based on edge computingZengrong ZhangIn order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. First, based on the comprehensive consideration of computing resources, bandwidth, and migration decisions, an optimization problem is constructed that minimizes the sum of all user task execution delays and energy consumption weights. Second, a dynamic offloading scheduling algorithm based on Q -learning is proposed based on the optimization problem. This algorithm makes full use of the computing power for cloud and edge, which effectively meets the demand for efficient computing services in Internet of Things’ scenarios. Finally, facing the environment dynamic changes of edge nodes in edge cloud, the algorithm can adaptively adjust the migration strategy. Experiments show that when the number of Internet of Things’ devices is 30, the total energy consumption of Internet of Things’ devices of proposed algorithm is reduced by 24.67% and 19.44%, respectively, compared with other algorithms. The experimental results show that proposed algorithm can effectively improve the success rate of task offloading and execution, which can reduce the local energy consumption.https://doi.org/10.1177/15501477211064800 |
| spellingShingle | Zengrong Zhang A computing allocation strategy for Internet of things’ resources based on edge computing International Journal of Distributed Sensor Networks |
| title | A computing allocation strategy for Internet of things’ resources based on edge computing |
| title_full | A computing allocation strategy for Internet of things’ resources based on edge computing |
| title_fullStr | A computing allocation strategy for Internet of things’ resources based on edge computing |
| title_full_unstemmed | A computing allocation strategy for Internet of things’ resources based on edge computing |
| title_short | A computing allocation strategy for Internet of things’ resources based on edge computing |
| title_sort | computing allocation strategy for internet of things resources based on edge computing |
| url | https://doi.org/10.1177/15501477211064800 |
| work_keys_str_mv | AT zengrongzhang acomputingallocationstrategyforinternetofthingsresourcesbasedonedgecomputing AT zengrongzhang computingallocationstrategyforinternetofthingsresourcesbasedonedgecomputing |