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：我们研究了一个包含异构移动用户和网络组件的大规模MEC系统中的节能卸载策略。该系统中启用了用户任务移交功能以捕捉各类移动用户的移动性。我们关注长期目标以及适用于现实系统的在线算法。由于问题规模庞大、用户任务和网络组件的异质性以及用户的移动性，传统优化器无法在合理的计算和存储能力下达到最优解，这使得问题变得极为复杂。我们采用类似非稳态多臂赌博机过程的方法对该问题进行建模，从而能够分解高维状态空间，并以在线方式实现适用于实际大规模问题的近最优算法。基于该非稳态赌博机技术，我们提出了两种卸载策略，通过优先选择边缘及云网络中相应计算和通信资源的最小边际成本来实现。这相当于选择能量效率最高的资源。两种策略均可扩展至大规模卸载问题，并具有实现已证明的渐近最优性的巨大潜力——即随着问题规模趋于无穷大，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，并且对测试中卸载任务的厚尾寿命分布具有鲁棒性。