GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computing
Edge computing is a key technology that provides computational resources close to IoT devices. One of the primary challenges in edge computing is determining whether to execute computation-intensive and time-sensitive tasks locally, or to offload them to edge and cloud computing resources, as well a...
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| Language: | English |
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025002828 |
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| author | Arezou Mahmoudi Leili Farzinvash Javid Taheri |
| author_facet | Arezou Mahmoudi Leili Farzinvash Javid Taheri |
| author_sort | Arezou Mahmoudi |
| collection | DOAJ |
| description | Edge computing is a key technology that provides computational resources close to IoT devices. One of the primary challenges in edge computing is determining whether to execute computation-intensive and time-sensitive tasks locally, or to offload them to edge and cloud computing resources, as well as to order them for execution according to their deadlines. Various offloading algorithms have been proposed for these systems, each with its own advantages and disadvantages. Several studies did not exploit all the IoT, edge, and cloud layers, whereas others only considered a few criteria for decision making on task offloading. Other approaches used greedy methods that could not provide high-quality solutions or employed standard optimization algorithms, which took a long time to converge. In this study, we propose an improved genetic algorithm for joint task offloading and ordering to distribute tasks across the IoT, edge, and cloud layers. It includes a novel population initialization scheme that uses various methods, including particle swarm optimization. To increase the convergence speed, the proposed algorithm (GPTOR) splits the solution space into several areas, which is called gridding. The simulation results illustrate that our algorithm outperforms previous schemes by 41.07%, 26.25%, and 28.33% in terms of average delay, monetary cost, and energy consumption, respectively. |
| format | Article |
| id | doaj-art-258aa9ca3fce4fcb9395a594870c672f |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-258aa9ca3fce4fcb9395a594870c672f2025-02-07T04:48:12ZengElsevierResults in Engineering2590-12302025-03-0125104196GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computingArezou Mahmoudi0Leili Farzinvash1Javid Taheri2Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, IranFaculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran; Corresponding author.Department of Mathematics and Computer Science, Karlstad University, Karlstad, Sweden; School of Electrical Engineering and Computer Science, Queen's University Belfast, UKEdge computing is a key technology that provides computational resources close to IoT devices. One of the primary challenges in edge computing is determining whether to execute computation-intensive and time-sensitive tasks locally, or to offload them to edge and cloud computing resources, as well as to order them for execution according to their deadlines. Various offloading algorithms have been proposed for these systems, each with its own advantages and disadvantages. Several studies did not exploit all the IoT, edge, and cloud layers, whereas others only considered a few criteria for decision making on task offloading. Other approaches used greedy methods that could not provide high-quality solutions or employed standard optimization algorithms, which took a long time to converge. In this study, we propose an improved genetic algorithm for joint task offloading and ordering to distribute tasks across the IoT, edge, and cloud layers. It includes a novel population initialization scheme that uses various methods, including particle swarm optimization. To increase the convergence speed, the proposed algorithm (GPTOR) splits the solution space into several areas, which is called gridding. The simulation results illustrate that our algorithm outperforms previous schemes by 41.07%, 26.25%, and 28.33% in terms of average delay, monetary cost, and energy consumption, respectively.http://www.sciencedirect.com/science/article/pii/S2590123025002828Edge computingTask offloadingTask orderingGenetic algorithmCustomized operatorsParticle swarm optimization |
| spellingShingle | Arezou Mahmoudi Leili Farzinvash Javid Taheri GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computing Results in Engineering Edge computing Task offloading Task ordering Genetic algorithm Customized operators Particle swarm optimization |
| title | GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computing |
| title_full | GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computing |
| title_fullStr | GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computing |
| title_full_unstemmed | GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computing |
| title_short | GPTOR: Gridded GA and PSO-based task offloading and ordering in IoT-edge-cloud computing |
| title_sort | gptor gridded ga and pso based task offloading and ordering in iot edge cloud computing |
| topic | Edge computing Task offloading Task ordering Genetic algorithm Customized operators Particle swarm optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025002828 |
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