A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User Cooperation
In the 5G era, the amount of network data has grown explosively. A large number of new computation-intensive applications have created demand for edge computing in mobile networks. Traditional optimization methods are difficult to adapt to the dynamic wireless network environment because they solve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/6/1459 |
| _version_ | 1797612181580152832 |
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| author | Yichen Jin Ziwei Chen |
| author_facet | Yichen Jin Ziwei Chen |
| author_sort | Yichen Jin |
| collection | DOAJ |
| description | In the 5G era, the amount of network data has grown explosively. A large number of new computation-intensive applications have created demand for edge computing in mobile networks. Traditional optimization methods are difficult to adapt to the dynamic wireless network environment because they solve the problem online, which is not suitable in edge computing scenarios. Therefore, in order to obtain a mobile network with better performance, we propose a network frame with a resource allocation algorithm based on power consumption, delay and user cooperation. This algorithm can quickly realize the optimization of a network to improve performance. Specifically, compared with heuristic algorithms, such as particle swarm optimization, ant colony algorithm, etc., commonly used to solve such problems, the algorithm proposed in this paper can reduce some aspects of network performance (including delay and user energy consumption) by about 10% in a network dominated by downlink tasks. The performance of the algorithm under certain network conditions was demonstrated through simulations. |
| first_indexed | 2024-03-11T06:37:41Z |
| format | Article |
| id | doaj.art-494d5c6d1521409399e246433f790930 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T06:37:41Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-494d5c6d1521409399e246433f7909302023-11-17T10:45:50ZengMDPI AGElectronics2079-92922023-03-01126145910.3390/electronics12061459A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User CooperationYichen Jin0Ziwei Chen1Department of Electrical and Electronic Engineering, The University of Hongkong, Hongkong 999077, ChinaDepartment of Electrical and Electronic Engineering, Beijing Jiaotong University, Beijing 100044, ChinaIn the 5G era, the amount of network data has grown explosively. A large number of new computation-intensive applications have created demand for edge computing in mobile networks. Traditional optimization methods are difficult to adapt to the dynamic wireless network environment because they solve the problem online, which is not suitable in edge computing scenarios. Therefore, in order to obtain a mobile network with better performance, we propose a network frame with a resource allocation algorithm based on power consumption, delay and user cooperation. This algorithm can quickly realize the optimization of a network to improve performance. Specifically, compared with heuristic algorithms, such as particle swarm optimization, ant colony algorithm, etc., commonly used to solve such problems, the algorithm proposed in this paper can reduce some aspects of network performance (including delay and user energy consumption) by about 10% in a network dominated by downlink tasks. The performance of the algorithm under certain network conditions was demonstrated through simulations.https://www.mdpi.com/2079-9292/12/6/1459mobile edge computingmachine learningresource allocationreinforcement learning |
| spellingShingle | Yichen Jin Ziwei Chen A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User Cooperation Electronics mobile edge computing machine learning resource allocation reinforcement learning |
| title | A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User Cooperation |
| title_full | A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User Cooperation |
| title_fullStr | A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User Cooperation |
| title_full_unstemmed | A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User Cooperation |
| title_short | A Fast Resource Allocation Algorithm Based on Reinforcement Learning in Edge Computing Networks Considering User Cooperation |
| title_sort | fast resource allocation algorithm based on reinforcement learning in edge computing networks considering user cooperation |
| topic | mobile edge computing machine learning resource allocation reinforcement learning |
| url | https://www.mdpi.com/2079-9292/12/6/1459 |
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