The rapid growth of mobile devices and the increasing complexity of tasks have made energy efficiency a critical challenge in Multi-Access Edge Computing (MEC) systems. This paper explores energy-efficient offloading strategies in large-scale MEC systems with heterogeneous mobile users, diverse network components, and frequent task handovers to capture user mobility. The problem is inherently complex due to the system's scale, task and resource diversity, and the need to maintain real-time performance. Traditional optimization approaches are often computationally infeasible for such scenarios. To tackle these challenges, we model the offloading problem using the restless multi-armed bandit (RMAB) framework and develop two scalable online policies that prioritize resources based on their marginal costs. The proposed policies dynamically adapt to the system's heterogeneity and mobility while ensuring near-optimal energy efficiency. Through extensive numerical simulations, we demonstrate that the policies significantly outperform baseline methods in power conservation and show robust performance under non-exponentially distributed task lifespans. These results highlight the practical applicability and scalability of our approach in dynamic MEC environments.

翻译：随着移动设备的快速增长和任务复杂度的日益提高，能源效率已成为多接入边缘计算（MEC）系统面临的关键挑战。本文研究了大规模MEC系统中考虑用户移动性、包含异构移动用户、多样化网络组件和频繁任务切换场景下的节能卸载策略。由于系统规模庞大、任务与资源多样且需维持实时性能，该问题本质十分复杂。传统优化方法在此类场景中往往计算不可行。为应对这些挑战，我们采用不安定多臂老虎机（RMAB）框架对卸载问题进行建模，并开发了两种基于边际成本进行资源优先级排序的可扩展在线策略。所提出的策略能动态适应系统异构性和移动性，同时确保接近最优的能源效率。通过大量数值仿真，我们证明该策略在节能方面显著优于基线方法，并在非指数分布的任务生命周期下表现出鲁棒性能。这些结果凸显了我们的方法在动态MEC环境中的实际适用性和可扩展性。