Multi-objective multi-workflow task offloading based on evolutionary optimization
Abstract Multi-access edge computing (MEC) addresses the constraints of limited computing resources and battery-limited devices by deploying computing resources at the network edge. However, most existing works on task offloading focus on single optimization objectives or simple tasks, neglecting th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00073-8 |
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| author | Teliekebieke Misha Lisheng Sun Zheng-yi Chai |
| author_facet | Teliekebieke Misha Lisheng Sun Zheng-yi Chai |
| author_sort | Teliekebieke Misha |
| collection | DOAJ |
| description | Abstract Multi-access edge computing (MEC) addresses the constraints of limited computing resources and battery-limited devices by deploying computing resources at the network edge. However, most existing works on task offloading focus on single optimization objectives or simple tasks, neglecting the complexity of task types and diverse requirements in real-world applications. To solve this problem, this paper investigates the multi-objective offloading problem for multi-workflow tasks in heterogeneous Internet of Things (IoT) scenarios. The aforementioned problem is formulated as a multi-objective optimization problem with two objectives: average task completion latency and device energy consumption. We propose an improved decomposition-based multi-objective evolutionary algorithm (MOEA/D) incorporating two novel strategies: (1) population initialization based on prior knowledge, and (2) a population distribution-based weight adjustment scheme. Simulation results demonstrate that our algorithm optimally balances delay and energy consumption requirements compared to existing methods, while enhancing the diversity and convergence of non-dominated solutions. |
| format | Article |
| id | doaj-art-6ee573c15eee43da90f666eb089090f0 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-6ee573c15eee43da90f666eb089090f02025-08-24T11:53:50ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-08-0137711410.1007/s44443-025-00073-8Multi-objective multi-workflow task offloading based on evolutionary optimizationTeliekebieke Misha0Lisheng Sun1Zheng-yi Chai2School of Network Security and Information Technology, Yili Normal UniversitySchool of Network Security and Information Technology, Yili Normal UniversityQuanzhou Vocational and Technical UniversityAbstract Multi-access edge computing (MEC) addresses the constraints of limited computing resources and battery-limited devices by deploying computing resources at the network edge. However, most existing works on task offloading focus on single optimization objectives or simple tasks, neglecting the complexity of task types and diverse requirements in real-world applications. To solve this problem, this paper investigates the multi-objective offloading problem for multi-workflow tasks in heterogeneous Internet of Things (IoT) scenarios. The aforementioned problem is formulated as a multi-objective optimization problem with two objectives: average task completion latency and device energy consumption. We propose an improved decomposition-based multi-objective evolutionary algorithm (MOEA/D) incorporating two novel strategies: (1) population initialization based on prior knowledge, and (2) a population distribution-based weight adjustment scheme. Simulation results demonstrate that our algorithm optimally balances delay and energy consumption requirements compared to existing methods, while enhancing the diversity and convergence of non-dominated solutions.https://doi.org/10.1007/s44443-025-00073-8Multi-access edge computingTask offloadingMulti-objective optimizationEvolutionary algorithm |
| spellingShingle | Teliekebieke Misha Lisheng Sun Zheng-yi Chai Multi-objective multi-workflow task offloading based on evolutionary optimization Journal of King Saud University: Computer and Information Sciences Multi-access edge computing Task offloading Multi-objective optimization Evolutionary algorithm |
| title | Multi-objective multi-workflow task offloading based on evolutionary optimization |
| title_full | Multi-objective multi-workflow task offloading based on evolutionary optimization |
| title_fullStr | Multi-objective multi-workflow task offloading based on evolutionary optimization |
| title_full_unstemmed | Multi-objective multi-workflow task offloading based on evolutionary optimization |
| title_short | Multi-objective multi-workflow task offloading based on evolutionary optimization |
| title_sort | multi objective multi workflow task offloading based on evolutionary optimization |
| topic | Multi-access edge computing Task offloading Multi-objective optimization Evolutionary algorithm |
| url | https://doi.org/10.1007/s44443-025-00073-8 |
| work_keys_str_mv | AT teliekebiekemisha multiobjectivemultiworkflowtaskoffloadingbasedonevolutionaryoptimization AT lishengsun multiobjectivemultiworkflowtaskoffloadingbasedonevolutionaryoptimization AT zhengyichai multiobjectivemultiworkflowtaskoffloadingbasedonevolutionaryoptimization |