Reinforcement learning-based joint task offloading and migration schemes optimization in mobility-aware MEC network

Intelligent edge computing carries out edge devices of the Internet of things (IoT) for data collection, calculation and intelligent analysis, so as to proceed data analysis nearby and make feedback timely. Because of the mobility of mobile equipments (MEs), if MEs move among the reach of the small cell networks (SCNs), the offloaded tasks cannot be returned to MEs successfully. As a result, migration incurs additional costs. In this paper, joint task offloading and migration schemes in mobility-aware Mobile Edge Computing (MEC) network based on Reinforcement Learning (RL) are proposed to obtain the maximum system revenue. Firstly, the joint optimization problems of maximizing the total revenue of MEs are put forward, in view of the mobility-aware MEs. Secondly, considering time-varying computation tasks and resource conditions, the mixed integer non-linear programming (MINLP) problem is described as a Markov Decision Process (MDP). Then we propose a novel reinforcement learning-based optimization framework to work out the problem, instead traditional methods. Finally, it is shown that the proposed schemes can obviously raise the total revenue of MEs by giving simulation results.