MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments
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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916846107623424 |
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| author | Mahdizadeh, Soheil Oustad, Elyas Ansari, Mohsen |
| author_facet | Mahdizadeh, Soheil Oustad, Elyas Ansari, Mohsen |
| contents | Task offloading in three-layer fog computing environments presents a critical challenge due to user equipment (UE) mobility, which frequently triggers costly service migrations and degrades overall system performance. This paper addresses this problem by proposing MOFCO, a novel Mobility- and Migration-aware Task Offloading algorithm for Fog Computing environments. The proposed method formulates task offloading and resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem and employs a heuristic-aided evolutionary game theory approach to solve it efficiently. To evaluate MOFCO, we simulate mobile users using SUMO, providing realistic mobility patterns. Experimental results show that MOFCO reduces system cost, defined as a combination of latency and energy consumption, by an average of 19% and up to 43% in certain scenarios compared to state-of-the-art methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_12028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments Mahdizadeh, Soheil Oustad, Elyas Ansari, Mohsen Hardware Architecture Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Task offloading in three-layer fog computing environments presents a critical challenge due to user equipment (UE) mobility, which frequently triggers costly service migrations and degrades overall system performance. This paper addresses this problem by proposing MOFCO, a novel Mobility- and Migration-aware Task Offloading algorithm for Fog Computing environments. The proposed method formulates task offloading and resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem and employs a heuristic-aided evolutionary game theory approach to solve it efficiently. To evaluate MOFCO, we simulate mobile users using SUMO, providing realistic mobility patterns. Experimental results show that MOFCO reduces system cost, defined as a combination of latency and energy consumption, by an average of 19% and up to 43% in certain scenarios compared to state-of-the-art methods. |
| title | MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments |
| topic | Hardware Architecture Distributed, Parallel, and Cluster Computing Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.12028 |