Optimal Design of Wireless-Powered Hierarchical Fog-Cloud Computing Networks

This paper investigates the optimal design of wireless- powered hierarchical fog-cloud computing networks, where energy-constrained users first harvest energy from a hybrid access point (HAP) and then offload their computation tasks to fog/cloud servers via the HAP or compute the tasks locally by us- ing the harvested energy. An optimization problem is formulated to maximize the minimal energy balance among multiple users by jointly optimizing offloading decisions, communication and computation resource allocations in the system, where computational capacity, processing delay, energy harvesting (EH) and energy consumption constraints are considered. To efficiently solve such a mixed-integer combinatorial non-convex problem, a penalized successive convex approximation (P-SCA)- based algorithm is designed, which is able to converge to a suboptimal solution with the polynomial time computational complexity. Numerical results show that compared to communication and computation resource allocation, the offloading decision is the dominant factor on affecting the system performance. It is also found that local computing is a better choice for users with relatively poor channel gains while fog/cloud computing is a better choice for users with relatively good channel gains. Specifically, cloud computing is preferred if the cloud computational capacity is strong enough and the wired-link data rate is high enough; Otherwise, fog computing is preferred. Besides, more users are served, less max-min energy balance can be obtained.