Wi-Fi网络下多AP协作边缘计算资源分配策略

近年来, AR/VR、在线游戏、4K/8K超高清视频等计算密集且时延敏感型应用不断涌现, 而部分移动设备受自身硬件条件的限制, 无法在时延要求内完成此类应用的计算, 且运行此类应用会带来巨大的能耗, 降低移动设备的续航能力. 为了解决这一问题, 本文提出了一种Wi-Fi网络多AP (access point)协作场景下边缘计算卸载和资源分配方案. 首先, 通过遗传算法确定用户的任务卸载决策. 随后, 利用匈牙利算法为进行任务卸载的用户分配通信资源. 最后, 根据任务处理时延限制, 为进行任务卸载的用户分配边缘服务器计算资源, 使其满足任务处理时延限制要求. 仿真结果表明, 所提出的任务卸载与资源分配方案能够在满足任务处理时延限制的前提下有效降低移动设备的能耗.

Resource Allocation Schemes of Edge Computing in Wi-Fi Network Supporting Multi-AP Coordination

In recent years, compute-intensive and time delay-sensitive applications such as AR/VR, online games, and 4K/8K ultra-high-resolution videos have been emerging. Due to the limitations of their hardware conditions, some mobile devices are unable to calculate such applications under the time-delay requirements, and running such applications will consume huge energy and reduce the endurance of mobile devices. To solve this problem, this study proposes an edge computing offloading and resource allocation scheme in a Wi-Fi network with the coordination of multiple access points (APs). Firstly, the genetic algorithm is utilized to determine the task offloading decision of users. Then, the Hungarian algorithm is used to allocate communication resources to users with task offloading. Finally, according to the time-delay limit of task processing, the computing resources of mobile edge computing (MEC) servers are allocated to the users with task offloading. The simulations reveal that the proposed task offloading and resource allocation scheme can effectively reduce the energy consumption of mobile devices on the premise of meeting the time-delay limit of task processing.