Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles
An edge computing offloading strategy was proposed with the goal of addressing the issue of low edge computing efficiency and service quality in the multi-service and multi-user intersections of networked vehicles. This strategy took into account all relevant factors, including the matching of users...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/10/6079 |
| _version_ | 1797601243997143040 |
|---|---|
| author | Hongwei Zhao Jingyue You Yangyang Wang Xike Zhao |
| author_facet | Hongwei Zhao Jingyue You Yangyang Wang Xike Zhao |
| author_sort | Hongwei Zhao |
| collection | DOAJ |
| description | An edge computing offloading strategy was proposed with the goal of addressing the issue of low edge computing efficiency and service quality in the multi-service and multi-user intersections of networked vehicles. This strategy took into account all relevant factors, including the matching of users and service nodes, offloading ratio, bandwidth and computing power resource allocation, and system energy consumption. It is mainly divided into 2 tasks: (1) Service node selection: A fuzzy logic-based service node selection algorithm (SNFLC) is proposed. The linear equation for node performance value is determined through fuzzy reasoning by specifying three performance indexes as input. Gradient descent method is used to find the optimal value of the objective function, and the Lyapunov criterion coefficient is introduced to improve the stability of the algorithm. (2) Offload ratio and resource allocation are solved: The coupling between offload ratio and bandwidth resource allocation is confirmed by relaxing integer variables because the optimization goal problem is a NP problem, and the issue is divided into two sub-problems. At the same time, a low-complexity alternate iteration resource allocation algorithm (LC-IRA) is proposed to solve the bandwidth resource and computational power resource allocation. According to simulation results, the performance of genetic ant colony algorithm (G_ACA), non orthogonal multiple access technology (NOMA) and LC-IRA are improved by 26.5%, 31.37%, and 45.52%, respectively, compared with the random unloading allocation (RUA) and average distribution (AD). |
| first_indexed | 2024-03-11T03:58:24Z |
| format | Article |
| id | doaj.art-28599c183267479e814af849a854d0ff |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:58:24Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-28599c183267479e814af849a854d0ff2023-11-18T00:20:12ZengMDPI AGApplied Sciences2076-34172023-05-011310607910.3390/app13106079Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of VehiclesHongwei Zhao0Jingyue You1Yangyang Wang2Xike Zhao3School of Information Engineering, Shenyang University, Shenyang 110044, ChinaSchool of Information Engineering, Shenyang University, Shenyang 110044, ChinaSchool of Information Engineering, Shenyang University, Shenyang 110044, ChinaSchool of Information Engineering, Shenyang University, Shenyang 110044, ChinaAn edge computing offloading strategy was proposed with the goal of addressing the issue of low edge computing efficiency and service quality in the multi-service and multi-user intersections of networked vehicles. This strategy took into account all relevant factors, including the matching of users and service nodes, offloading ratio, bandwidth and computing power resource allocation, and system energy consumption. It is mainly divided into 2 tasks: (1) Service node selection: A fuzzy logic-based service node selection algorithm (SNFLC) is proposed. The linear equation for node performance value is determined through fuzzy reasoning by specifying three performance indexes as input. Gradient descent method is used to find the optimal value of the objective function, and the Lyapunov criterion coefficient is introduced to improve the stability of the algorithm. (2) Offload ratio and resource allocation are solved: The coupling between offload ratio and bandwidth resource allocation is confirmed by relaxing integer variables because the optimization goal problem is a NP problem, and the issue is divided into two sub-problems. At the same time, a low-complexity alternate iteration resource allocation algorithm (LC-IRA) is proposed to solve the bandwidth resource and computational power resource allocation. According to simulation results, the performance of genetic ant colony algorithm (G_ACA), non orthogonal multiple access technology (NOMA) and LC-IRA are improved by 26.5%, 31.37%, and 45.52%, respectively, compared with the random unloading allocation (RUA) and average distribution (AD).https://www.mdpi.com/2076-3417/13/10/6079edge computingfuzzy logic reasoningLyapunovresource allocation |
| spellingShingle | Hongwei Zhao Jingyue You Yangyang Wang Xike Zhao Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles Applied Sciences edge computing fuzzy logic reasoning Lyapunov resource allocation |
| title | Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles |
| title_full | Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles |
| title_fullStr | Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles |
| title_full_unstemmed | Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles |
| title_short | Offloading Strategy of Multi-Service and Multi-User Edge Computing in Internet of Vehicles |
| title_sort | offloading strategy of multi service and multi user edge computing in internet of vehicles |
| topic | edge computing fuzzy logic reasoning Lyapunov resource allocation |
| url | https://www.mdpi.com/2076-3417/13/10/6079 |
| work_keys_str_mv | AT hongweizhao offloadingstrategyofmultiserviceandmultiuseredgecomputingininternetofvehicles AT jingyueyou offloadingstrategyofmultiserviceandmultiuseredgecomputingininternetofvehicles AT yangyangwang offloadingstrategyofmultiserviceandmultiuseredgecomputingininternetofvehicles AT xikezhao offloadingstrategyofmultiserviceandmultiuseredgecomputingininternetofvehicles |