Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing
With mobile edge computing, vehicles can obtain nearby network resources and computability and meet the growing demand for vehicular service at large scales. However, as a result of vehicle mobility and offloading of an extensive number of tasks, the congestion in wireless networks and the insuffici...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157822003780 |
| _version_ | 1811184017592025088 |
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| author | Mohamed A. Alqarni Mohamed H. Mousa Mohamed K. Hussein |
| author_facet | Mohamed A. Alqarni Mohamed H. Mousa Mohamed K. Hussein |
| author_sort | Mohamed A. Alqarni |
| collection | DOAJ |
| description | With mobile edge computing, vehicles can obtain nearby network resources and computability and meet the growing demand for vehicular service at large scales. However, as a result of vehicle mobility and offloading of an extensive number of tasks, the congestion in wireless networks and the insufficient computing resources of edge servers make it difficult to maintain good service quality for users. Moreover, network access point selection is not often considered a factor in task execution latency. In this paper, we propose a smart metaheuristic optimization model to address the problem of low service quality due to vehicle movements and limited edge coverage. Then, the proposed model is used to characterize the overall latency of vehicle task offloading by considering resource utilization, workload at edge servers and vehicle movement characteristics. There are two advantages of our proposed framework. First, the design of the optimization model offers an adaptive task offloading strategy by automatically providing preallocation decisions with respect to the edge server workload state. Second, the proposed metaheuristic approach benefits from recent advances in graphics processing unit (GPU) architectures. In fact, the design of the PSO on a GPU shifts the optimization of the task offloading process to a promising area in terms of time and precision. Extensive experimental results are presented to demonstrate the effectiveness of the proposed framework. |
| first_indexed | 2024-04-11T13:06:17Z |
| format | Article |
| id | doaj.art-955ebc9a731240cd83d8686ca31ae125 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-11T13:06:17Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-955ebc9a731240cd83d8686ca31ae1252022-12-22T04:22:45ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-11-0134101035610364Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computingMohamed A. Alqarni0Mohamed H. Mousa1Mohamed K. Hussein2Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaDepartment of Information Technology, College of Computing & Information Technology at AlKamil, University of Jeddah, Jeddah, Saudi Arabia; Corresponding author.Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, EgyptWith mobile edge computing, vehicles can obtain nearby network resources and computability and meet the growing demand for vehicular service at large scales. However, as a result of vehicle mobility and offloading of an extensive number of tasks, the congestion in wireless networks and the insufficient computing resources of edge servers make it difficult to maintain good service quality for users. Moreover, network access point selection is not often considered a factor in task execution latency. In this paper, we propose a smart metaheuristic optimization model to address the problem of low service quality due to vehicle movements and limited edge coverage. Then, the proposed model is used to characterize the overall latency of vehicle task offloading by considering resource utilization, workload at edge servers and vehicle movement characteristics. There are two advantages of our proposed framework. First, the design of the optimization model offers an adaptive task offloading strategy by automatically providing preallocation decisions with respect to the edge server workload state. Second, the proposed metaheuristic approach benefits from recent advances in graphics processing unit (GPU) architectures. In fact, the design of the PSO on a GPU shifts the optimization of the task offloading process to a promising area in terms of time and precision. Extensive experimental results are presented to demonstrate the effectiveness of the proposed framework.http://www.sciencedirect.com/science/article/pii/S1319157822003780High-performance computingGPUParticle swarm optimizationTask offloadingAdaptive preallocation |
| spellingShingle | Mohamed A. Alqarni Mohamed H. Mousa Mohamed K. Hussein Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing Journal of King Saud University: Computer and Information Sciences High-performance computing GPU Particle swarm optimization Task offloading Adaptive preallocation |
| title | Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing |
| title_full | Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing |
| title_fullStr | Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing |
| title_full_unstemmed | Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing |
| title_short | Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing |
| title_sort | task offloading using gpu based particle swarm optimization for high performance vehicular edge computing |
| topic | High-performance computing GPU Particle swarm optimization Task offloading Adaptive preallocation |
| url | http://www.sciencedirect.com/science/article/pii/S1319157822003780 |
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