Optimal Two-Timescale Configuration of Mobile Edge Computing With Mixed Energy Supply

Powering mobile edge computing (MEC) with a hybrid supply of smart grid (SG) and renewable energy source (RES) offers an opportunity to utilize clean energy and cut down energy expenses under two-way energy transactions. We propose two-timescale online resource allocation and energy management (TSRE) for MEC with a hybrid power supply, adapting to dynamic task and RES arrivals, wireless channels, and energy prices. The TSRE minimizes the time-averaged cost of predictive energy planning and real-time energy trading of base stations (BSs), and the energy usage of mobile users. By generalizing the Lyapunov optimization and stochastic subgradient method, the energy planning (sub)problem is solved upon RES arrivals using only historical data. The real-time offloading schedules, energy trading decisions and CPU configurations are decoupled over time and (asymptotically) optimally made in a distributed manner. The feasibility and the asymptotic optimality of the TSRE are proved. Numerical results demonstrate that the TSRE saves system cost significantly by 33.7%, compared to its baselines.

I. Introduction

The development of the Internet of Everything (IoE) calls for the explosive requirements of powerful computing capacity and low-latency communications. Mobile edge computing (MEC), promoted by the European Telecommunication Standards Association in 2014 [1], can effectively address this challenge by deploying edge servers in close vicinity to mobile users. By offloading computation tasks to the edge of the network, MEC bridges the gap between computationally demanding services and resource-constrained mobile terminals. With the rising demand for MEC, the energy expenses make up a significant portion of the operational expenditure (OPEX) of operators.