Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing
The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic co...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2021452?viewType=HTML |
| _version_ | 1818993123436527616 |
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| author | Yanpei Liu Wei Huang Liping Wang Yunjing Zhu Ningning Chen |
| author_facet | Yanpei Liu Wei Huang Liping Wang Yunjing Zhu Ningning Chen |
| author_sort | Yanpei Liu |
| collection | DOAJ |
| description | The current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS. |
| first_indexed | 2024-12-20T20:37:03Z |
| format | Article |
| id | doaj.art-60386ebcf843400baff865b7898c0e7d |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-20T20:37:03Z |
| publishDate | 2021-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-60386ebcf843400baff865b7898c0e7d2022-12-21T19:27:13ZengAIMS PressMathematical Biosciences and Engineering1551-00182021-10-011869163918910.3934/mbe.2021452Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computingYanpei Liu 0Wei Huang1Liping Wang2Yunjing Zhu3Ningning Chen4School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaThe current computation offloading algorithm for the mobile cloud ignores the selection of offloading opportunities and does not consider the uninstall frequency, resource waste, and energy efficiency reduction of the user's offloading success probability. Therefore, in this study, a dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in a multi-access edge computing environment is proposed (DCO-PSOMO). According to the CPU utilization and the memory utilization rate of the mobile terminal, this method can dynamically obtain the overload time by using a strong, locally weighted regression method. After detecting the overload time, the probability of successful downloading is predicted by the mobile user's dwell time and edge computing communication range, and the offloading is either conducted immediately or delayed. A computation offloading model was established via the use of the response time and energy consumption of the mobile terminal. Additionally, the optimal computing offloading algorithm was designed via the use of a particle swarm with a mutation operator. Finally, the DCO-PSOMO algorithm was compared with the JOCAP, ECOMC and ESRLR algorithms, and the experimental results demonstrated that the DCO-PSOMO offloading method can effectively reduce the offloading cost and terminal energy consumption, and improves the success probability of offloading and the user's QoS.https://www.aimspress.com/article/doi/10.3934/mbe.2021452?viewType=HTMLcomputation offloadingmulti-access edge computingoffloading success rateoverload time |
| spellingShingle | Yanpei Liu Wei Huang Liping Wang Yunjing Zhu Ningning Chen Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing Mathematical Biosciences and Engineering computation offloading multi-access edge computing offloading success rate overload time |
| title | Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing |
| title_full | Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing |
| title_fullStr | Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing |
| title_full_unstemmed | Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing |
| title_short | Dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi-access edge computing |
| title_sort | dynamic computation offloading algorithm based on particle swarm optimization with a mutation operator in multi access edge computing |
| topic | computation offloading multi-access edge computing offloading success rate overload time |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2021452?viewType=HTML |
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