We consider the problem of task offloading in multi-access edge computing (MEC) systems constituting $N$ devices assisted by an edge server (ES), where the devices can split task execution between a local processor and the ES. Since the local task execution and communication with the ES both consume power, each device must judiciously choose between the two. We model the problem as a large population non-cooperative game among the $N$ devices. Since computation of an equilibrium in this scenario is difficult due to the presence of a large number of devices, we employ the mean-field game framework to reduce the finite-agent game problem to a generic user's multi-objective optimization problem, with a coupled consistency condition. By leveraging the novel age of information (AoI) metric, we invoke techniques from stochastic hybrid systems (SHS) theory and study the tradeoffs between increasing information freshness and reducing power consumption. In numerical simulations, we validate that a higher load at the ES may lead devices to upload their task to the ES less often.

翻译：我们考虑多接入边缘计算（MEC）系统中的任务卸载问题，该系统由$N$个设备和一个边缘服务器（ES）辅助构成，其中设备可以在本地处理器与ES之间分割任务执行。由于本地任务执行和与ES通信均会消耗能量，每个设备必须审慎地在两者之间做出选择。我们将该问题建模为$N$个设备间的大规模非合作博弈。由于在大量设备存在的情况下难以计算均衡解，我们采用平均场博弈框架将有限代理博弈问题简化为一个带有耦合一致性条件的通用用户多目标优化问题。通过利用新颖的信息年龄（AoI）度量指标，我们引入随机混合系统（SHS）理论的技术，并研究提升信息新鲜度与降低能耗之间的权衡关系。数值仿真验证了：当ES负载较高时，设备可能会减少向ES上传任务的频率。