标题： MOFCO：三层雾计算环境中的移动性和迁移感知任务卸载 显示英文标题

标题： MOFCO: Mobility- and Migration-Aware Task Offloading in Three-Layer Fog Computing Environments

摘要： 任务卸载在三层雾计算环境中由于用户设备（UE）的移动性而面临一个关键挑战，这经常引发成本高昂的服务迁移并降低整体系统性能。 本文通过提出MOFCO，一种针对雾计算环境的新型考虑移动性和迁移的任务卸载算法来解决这个问题。 所提出的方法将任务卸载和资源分配建模为一个混合整数非线性规划（MINLP）问题，并采用启发式辅助的进化博弈论方法来高效求解。 为了评估MOFCO，我们使用SUMO模拟移动用户，提供现实的移动模式。 实验结果表明，与最先进的方法相比，MOFCO在特定场景中平均将系统成本（定义为延迟和能耗的组合）降低了19%，最高可达43%。 显示英文摘要

摘要： 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.