Being an up-and-coming application scenario of mobile edge computing (MEC), the post-disaster rescue suffers multitudinous computing-intensive tasks but unstably guaranteed network connectivity. In rescue environments, quality of service (QoS), such as task execution delay, energy consumption and battery state of health (SoH), is of significant meaning. This paper studies a multi-user post-disaster MEC environment with unstable 5G communication, where device-to-device (D2D) link communication and dynamic voltage and frequency scaling (DVFS) are adopted to balance each user's requirement for task delay and energy consumption. A battery degradation evaluation approach to prolong battery lifetime is also presented. The distributed optimization problem is formulated into a mixed cooperative-competitive (MCC) multi-agent Markov decision process (MAMDP) and is tackled with recurrent multi-agent Proximal Policy Optimization (rMAPPO). Extensive simulations and comprehensive comparisons with other representative algorithms clearly demonstrate the effectiveness of the proposed rMAPPO-based offloading scheme.

翻译：移动边缘计算（MEC）作为新兴应用场景，灾后救援面临大量计算密集型任务，但网络连接稳定性难以保证。在救援环境中，服务质量（QoS）指标如任务执行延迟、能耗及电池健康状态（SoH）具有重要意义。本文研究采用不稳定的5G通信的多用户灾后MEC环境，通过设备直连（D2D）链路通信与动态电压频率调节（DVFS）技术平衡各用户对任务延迟与能耗的需求，并提出一种延长电池寿命的电池退化评估方法。将分布式优化问题建模为混合合作-竞争（MCC）多智能体马尔可夫决策过程（MAMDP），并采用循环多智能体近端策略优化（rMAPPO）算法求解。大量仿真实验及与代表性算法的全面对比，充分验证了所提基于rMAPPO的卸载方案的有效性。