Fan Xiaoqian, Zheng Haina, Jiang Ruihong, Zhang Jinyu
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel). 2020 Mar 12;20(6):1582. doi: 10.3390/s20061582.
This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.
本文研究了一种分层云-雾边缘计算（FEC）网络的优化设计，该网络由三层组成，即云层、雾边缘层和设备层。设备层中的设备通过三种计算模式处理其任务，即缓存辅助计算模式、云辅助计算模式和联合设备-雾边缘计算模式。具体而言，任务分别对应于通过FEC层中的内容缓存、向云层的计算卸载以及雾边缘层和设备层中的联合计算来完成。对于这样一个系统，通过联合优化计算模式选择、本地计算比率、计算频率和发射功率，同时保证多个系统约束，包括任务完成截止时间、可实现的计算能力和可实现的发射功率阈值，来制定能量最小化问题。由于该问题是一个混合整数非线性规划问题，难以用已知的标准方法求解，因此将其分解为三个子问题，并推导了每个子问题的最优解。然后，提出了一种高效的最优缓存、云与联合计算（CCJ）算法来解决主要问题。仿真结果表明，我们提出的优化设计所实现的系统性能优于基准方案所实现的性能。此外，设备的可实现发射功率阈值越小，节省的能量就越多。此外，随着任务数据大小的增加，本地计算比率越小。
