Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer
Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/15/12/2206 |
| _version_ | 1797379316604993536 |
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| author | Fengqing Tian Donghua Zhang Ying Yuan Guangchun Fu Xiaomin Li Guanghua Chen |
| author_facet | Fengqing Tian Donghua Zhang Ying Yuan Guangchun Fu Xiaomin Li Guanghua Chen |
| author_sort | Fengqing Tian |
| collection | DOAJ |
| description | Due to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system. |
| first_indexed | 2024-03-08T20:20:24Z |
| format | Article |
| id | doaj.art-c87173462ced4959998509327d23c8dc |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-08T20:20:24Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-c87173462ced4959998509327d23c8dc2023-12-22T14:45:24ZengMDPI AGSymmetry2073-89942023-12-011512220610.3390/sym15122206Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion OptimizerFengqing Tian0Donghua Zhang1Ying Yuan2Guangchun Fu3Xiaomin Li4Guanghua Chen5Henan Institute of Science and Technology, Xinxiang 453000, ChinaHenan Institute of Science and Technology, Xinxiang 453000, ChinaHenan Institute of Science and Technology, Xinxiang 453000, ChinaHenan Institute of Science and Technology, Xinxiang 453000, ChinaHenan Institute of Science and Technology, Xinxiang 453000, ChinaHenan Institute of Science and Technology, Xinxiang 453000, ChinaDue to the problem of large latency and energy consumption of fog computing in smart community applications, the fog computing task-scheduling method based on Hybrid Ant Lion Optimizer (HALO) is proposed in this paper. This method is based on the Ant Lion Optimizer (ALO. Firstly, chaotic mapping is adopted to initialize the population, and the quality of the initial population is improved; secondly, the Adaptive Random Wandering (ARW) method is designed to improve the solution efficiency; finally, the improved Dynamic Opposite Learning Crossover (DOLC) strategy is embedded in the generation-hopping stage of the ALO to enrich the diversity of the population and improve the optimization-seeking ability of ALO. HALO is used to optimize the scheduling scheme of fog computing tasks. The simulation experiments are conducted under different data task volumes, compared with several other task scheduling algorithms such as the original algorithm of ALO, Genetic Algorithm (GA), Whale Optimizer Algorithm (WOA) and Salp Swarm Algorithm (SSA). HALO has good initial population quality, fast convergence speed, and high optimization-seeking accuracy. The scheduling scheme obtained by the proposed method in this paper can effectively reduce the latency of the system and reduce the energy consumption of the system.https://www.mdpi.com/2073-8994/15/12/2206smart communityfog computingtask schedulingant lion optimizerlatencyenergy consumption |
| spellingShingle | Fengqing Tian Donghua Zhang Ying Yuan Guangchun Fu Xiaomin Li Guanghua Chen Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer Symmetry smart community fog computing task scheduling ant lion optimizer latency energy consumption |
| title | Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer |
| title_full | Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer |
| title_fullStr | Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer |
| title_full_unstemmed | Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer |
| title_short | Fog Computing Task Scheduling of Smart Community Based on Hybrid Ant Lion Optimizer |
| title_sort | fog computing task scheduling of smart community based on hybrid ant lion optimizer |
| topic | smart community fog computing task scheduling ant lion optimizer latency energy consumption |
| url | https://www.mdpi.com/2073-8994/15/12/2206 |
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