Research on elite hierarchical task offloading strategy based on reinforcement learning in edge-cloud collaboration scenario
With the development of 5G and the enrichment of application functions, applications have put forward higher requirements on the computing capabilities of terminal devices.In order to improve the computing capabilities of terminal devices on applications and reduce the processing time of tasks, it i...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
China InfoCom Media Group
2022-03-01
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| Series: | 物联网学报 |
| Subjects: | |
| Online Access: | http://www.wlwxb.com.cn/thesisDetails#10.11959/j.issn.2096-3750.2022.00258 |
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| Summary: | With the development of 5G and the enrichment of application functions, applications have put forward higher requirements on the computing capabilities of terminal devices.In order to improve the computing capabilities of terminal devices on applications and reduce the processing time of tasks, it is aimed at mobile edge computing environments, a task offloading method for edge-cloud collaboration was proposed，and an elite hierarchical evolutionary algorithm combined with reinforcement learning (RL-EHEA) was designed to perform offloading decisions, so that multiple tasks with dependencies and deadlines compete for computing resources.The simulation experiment results show that, compared with genetic algorithm (GA) and elite genetic algorithm (EGA), RL-EHEA can shorten task processing time and obtain better resource allocation strategy. |
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| ISSN: | 2096-3750 |