Fog-computing network combines the cloud computing and fog access points (FAPs) equipped with mobile edge computing (MEC) servers together to support computation-intensive tasks for mobile users. However, as FAPs have limited computational capabilities and are solely assisted by a remote cloud center in the baseband processing unit (BBU) of the cloud radio access (C-RAN) network, the latency benefits of this fog-computing C-RAN network may be worn off when facing a large number of offloading requests. In this paper, we investigate the delay minimization problem for task offloading in a hierarchical fog-computing C-RAN network, which consists of three tiers of computational services: MEC server in radio units, MEC server in distributed units, and the cloud computing in central units. The receive beamforming vectors, task allocation, computing speed for offloaded tasks in each server and the transmission bandwidth split of fronthaul links are optimized by solving the formulated mixed integer programming problem. The simulation results validate the superiority of the proposed hierarchical fog-computing C-RAN network in terms of the delay performance.

中文翻译：

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分层雾计算 C-RAN 网络中任务卸载的延迟最小化

雾计算网络将云计算和配备移动边缘计算（MEC）服务器的雾接入点（FAP）结合在一起，以支持移动用户的计算密集型任务。然而，由于 FAP 的计算能力有限，并且仅由云无线电接入 (C-RAN) 网络的基带处理单元 (BBU) 中的远程云中心协助，这种雾计算 C-RAN 网络的延迟优势可能在面临大量卸载请求时被磨损。在本文中，我们研究了分层雾计算 C-RAN 网络中任务卸载的延迟最小化问题，该网络由三层计算服务组成：无线电单元中的 MEC 服务器、分布式单元中的 MEC 服务器和云计算。中央单位。接收波束成形向量、任务分配、通过求解公式化的混合整数规划问题，优化了每个服务器中卸载任务的计算速度和前传链路的传输带宽拆分。仿真结果验证了所提出的分层雾计算 C-RAN 网络在延迟性能方面的优越性。