Abstract: In the energy harvesting based mobile edge computing (MEC) system, the energy arrivals and wireless channels for computing offloading are both dynamically changing in time and space, which results in dynamic adaptation between communication/computational resource management and task execution. To address such problems, based on the criterion of maximizing the system’s computing throughput, the predication models for renewable energy random arrival and wireless channel are established, and a novel online design framework is proposed for dynamically managing communication/computation resources over time. This solution solves the convex optimization problem time slot by time slot, and based on the optimal structure of offline resource dynamic management and control, real-time resource management strategies are formulated, and it has low computational complexity. Numerical results show that the proposed online sliding window design scheme is superior to the existing benchmark schemes in terms of system computational throughput performance, and has better robust performance against channel/energy state information prediction errors.

Key words: mobile edge computing, energy harvesting, computation offloading, online sliding-window design