A deep reinforcement approach for computation offloading in MEC dynamic networks
Abstract In this study, we investigate the challenges associated with dynamic time slot server selection in mobile edge computing (MEC) systems. This study considers the fluctuating nature of user access at edge servers and the various factors that influence server workload, including offloading pol...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-04-01
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| Series: | EURASIP Journal on Advances in Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13634-024-01142-2 |
| _version_ | 1797208997233688576 |
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| author | Yibiao Fan Xiaowei Cai |
| author_facet | Yibiao Fan Xiaowei Cai |
| author_sort | Yibiao Fan |
| collection | DOAJ |
| description | Abstract In this study, we investigate the challenges associated with dynamic time slot server selection in mobile edge computing (MEC) systems. This study considers the fluctuating nature of user access at edge servers and the various factors that influence server workload, including offloading policies, offloading ratios, users’ transmission power, and the servers’ reserved capacity. To streamline the process of selecting edge servers with an eye on long-term optimization, we cast the problem as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL)-based algorithm as a solution. Our approach involves learning the selection strategy by analyzing the performance of server selections in previous iterations. Simulation outcomes show that our DRL-based algorithm surpasses benchmarks, delivering minimal average latency. |
| first_indexed | 2024-04-24T09:47:41Z |
| format | Article |
| id | doaj.art-6c013ac2b3ec418197beb4191691abaa |
| institution | Directory Open Access Journal |
| issn | 1687-6180 |
| language | English |
| last_indexed | 2024-04-24T09:47:41Z |
| publishDate | 2024-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Advances in Signal Processing |
| spelling | doaj.art-6c013ac2b3ec418197beb4191691abaa2024-04-14T11:32:33ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61802024-04-012024111910.1186/s13634-024-01142-2A deep reinforcement approach for computation offloading in MEC dynamic networksYibiao Fan0Xiaowei Cai1School of Physics and Mechanical and Electrical Engineering, Longyan UniversitySchool of Physics and Mechanical and Electrical Engineering, Longyan UniversityAbstract In this study, we investigate the challenges associated with dynamic time slot server selection in mobile edge computing (MEC) systems. This study considers the fluctuating nature of user access at edge servers and the various factors that influence server workload, including offloading policies, offloading ratios, users’ transmission power, and the servers’ reserved capacity. To streamline the process of selecting edge servers with an eye on long-term optimization, we cast the problem as a Markov Decision Process (MDP) and propose a Deep Reinforcement Learning (DRL)-based algorithm as a solution. Our approach involves learning the selection strategy by analyzing the performance of server selections in previous iterations. Simulation outcomes show that our DRL-based algorithm surpasses benchmarks, delivering minimal average latency.https://doi.org/10.1186/s13634-024-01142-2Edge serversDynamic usersComputation offloadingDynamic tasksReinforcement learning |
| spellingShingle | Yibiao Fan Xiaowei Cai A deep reinforcement approach for computation offloading in MEC dynamic networks EURASIP Journal on Advances in Signal Processing Edge servers Dynamic users Computation offloading Dynamic tasks Reinforcement learning |
| title | A deep reinforcement approach for computation offloading in MEC dynamic networks |
| title_full | A deep reinforcement approach for computation offloading in MEC dynamic networks |
| title_fullStr | A deep reinforcement approach for computation offloading in MEC dynamic networks |
| title_full_unstemmed | A deep reinforcement approach for computation offloading in MEC dynamic networks |
| title_short | A deep reinforcement approach for computation offloading in MEC dynamic networks |
| title_sort | deep reinforcement approach for computation offloading in mec dynamic networks |
| topic | Edge servers Dynamic users Computation offloading Dynamic tasks Reinforcement learning |
| url | https://doi.org/10.1186/s13634-024-01142-2 |
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