Online Optimization of Energy-Efficient User Association and Workload Offloading for Mobile Edge Computing

Abstract

This paper presents a new stochastic optimization framework for user association and task offloading in mobile edge computing (MEC) networks with spatial and temporal variations of computing power, channel quality and connection capacity between different MEC servers. The new framework minimizes a quadratic penalty function which balances the energy consumption and fairness of the devices. Lyapunov optimization is first applied to eliminate the time coupling of the framework, leading to a mixed-integer program (MIP) of user association and offloading scheduling at every time slot. While solving the scheduling using linear programming, we convert the user association to a minimum cost maximum flow problem by interpreting edge servers and devices as two disjoint vertexes. We solve the minimum-cost maximum flow problem efficiently by using the Ford-Fulkerson algorithm. Corroborated by simulations, the proposed approach is asymptotically optimal and outperforms alternative approaches in terms of energy saving and fairness.