Two Time-Scale Energy and Spectrum Allocation for MEC Networks with Hybrid Energy Supplies

Abstract

Integrating power grid and renewable energy to charge mobile-edge computing (MEC) server is a promising solution for green networks, which can reduce the energy consumption of power grid and carbon emission. Intelligent management of energy and bandwidth resources in the two time-scale framework can increase the energy efficiency and network revenue in MEC networks with heterogeneous energy supplies and multiple mobile users (MUs). In this paper, we investigate the joint energy management (for workloads offloading and execution) and bandwidth allocation (for workloads transmission) problem that maximizes the network revenue in the MEC network with hybrid energy supplies. We formulate it into a challenging nonlinear optimization problem because of the data randomness and the temporal coupling effect. Based on the Lyapunov optimization approach with quick convergence speed and low complexity, we propose an online algorithm to overcome the obstacles by relaxing the battery constraints, allowing us to achieve close-to-optimal maximum network revenue with the stability of battery level and different throughput requirements of MUs. Extensive numeral simulations verify the theoretical analysis and demonstrate the superior performance of the proposed algorithm in different algorithms and scenarios.