Online Task Offloading with Edge Service Providers Selection for Mobile Edge Computing

Abstract

Mobile Edge Computing (MEC) is a promising distributed computing paradigm, where the service providers deploy their computing power at the communication base stations close to mobile users. By providing task offloading at the network edge devices, the edge service providers can significantly reduce end-to-end latency and improve user satisfaction. However, there usually exists multiple providers in one edge, mobile users will face the choice of which edge service provider to offload their computing tasks after user allocation and offloading decision to a certain base station. In this study, from the perspective of MEC system, we investigate the online task offloading with edge service provider selection problem. We model it as a stochastic optimization problem, aiming to maximize the long-term time average user utility under limited resources, while ensuring the stability of the MEC system. We propose an online algorithm based on Lyapunov optimization to achieve a good trade-off between user satisfaction, energy consumption and system stability, then give theoretical proof of its performance bound. Experiments have been conducted based on a real-world dataset, and the results show that OEPS shows full-range performance advantages over three baseline methods.