  Mobile Edge Computing (MEC) (a.k.a. fog computing) has recently emerged to
enable low-latency and location-aware data processing at the edge of mobile
networks. Since providing grid power supply in support of MEC can be costly and
even infeasible in some scenarios, on-site renewable energy is mandated as a
major or even sole power supply. Nonetheless, the high intermittency and
unpredictability of energy harvesting creates many new challenges of performing
effective MEC. In this paper, we develop an algorithm called GLOBE that
performs joint geographical load balancing (GLB) and admission control for
optimizing the system performance of a network of MEC-enabled and energy
harvesting-powered base stations. By leveraging and extending the Lyapunov
optimization with perturbation technique, GLOBE operates online without
requiring future system information and addresses significant challenges caused
by battery state dynamics and energy causality constraints. Moreover, GLOBE
works in a distributed manner, which makes our algorithm scalable to large
networks. We prove that GLOBE achieves a close-to-optimal system performance
compared to the offline algorithm that knows full future information, and
present a critical tradeoff between battery capacity and system performance.
Simulation results validate our analysis and demonstrate the superior
performance of GLOBE compared to benchmark algorithms.
