We study energy-efficient offloading strategies in a large-scale MEC system with heterogeneous mobile users and network components. The system is considered with enabled user-task handovers that capture the mobility of various mobile users. We focus on a long-run objective and online algorithms that are applicable to realistic systems. The problem is significantly complicated by the large problem size, the heterogeneity of user tasks and network components, and the mobility of the users, for which conventional optimizers cannot reach optimum with a reasonable amount of computational and storage power. We formulate the problem in the vein of the restless multi-armed bandit process that enables the decomposition of high-dimensional state spaces and then achieves near-optimal algorithms applicable to realistically large problems in an online manner. Following the restless bandit technique, we propose two offloading policies by prioritizing the least marginal costs of selecting the corresponding computing and communication resources in the edge and cloud networks. This coincides with selecting the resources with the highest energy efficiency. Both policies are scalable to the offloading problem with a great potential to achieve proved asymptotic optimality - approach optimality as the problem size tends to infinity. With extensive numerical simulations, the proposed policies are demonstrated to clearly outperform baseline policies with respect to power conservation and robust to the tested heavy-tailed lifespan distributions of the offloaded tasks.

翻译：本文研究了一个包含异构移动用户与网络组件的大规模多接入边缘计算（MEC）系统中的能效卸载策略。该系统中启用了用户任务交接功能，以捕捉各类移动用户的移动性。我们聚焦于长期优化目标和适用于现实系统的在线算法。由于问题规模庞大、用户任务与网络组件的异构性以及用户的移动性，传统优化器无法在合理的计算与存储开销下达到最优解。为此，我们基于非平稳多臂赌博机过程对问题进行建模，该模型能够实现高维状态空间的分解，并在线方式下获得适用于实际大规模问题的近优算法。遵循非平稳赌博机技术，我们通过优先选择边缘与云网络中计算与通信资源对应的最小边际成本，提出了两种卸载策略。这等价于选择能效最高的资源。两种策略均具有良好的可扩展性，并具备已被证明的渐近最优性潜力——即当问题规模趋于无穷时，策略趋近于最优解。通过大量数值仿真，所提策略在节能方面明显优于基线策略，且对卸载任务的重尾寿命分布具有鲁棒性。