Context-Aware Online Offloading Strategy with Mobility Prediction for Mobile Edge Computing

With the development of 5G technology and the proliferation of various mobile applications, mobile edge computing (MEC) provides services near the user side to meet the quality constraints of different tasks. Most current works focus on the offloading decision and resource allocation issues in MEC. However, few works focus on user mobility and the personalized preferences of different applications. In this paper, we study these issues and propose a deep reinforcement learning (DRL) based context-aware online offloading strategy. To further reduce the overhead caused by user mobility for task offloading and migration, we consider the user's future movement trajectory and calculate the potential migration cost. Considering the dynamic network environment and the incompleteness of the observed system state information, we formulate the offloading decision problem as a partially observable Markov decision process (POMDP) problem, and then devise an efficient DRL algorithm to speed it up. We use EdgeCloudSim tool and Geolife trajectory to simulate the task offloading decision problem. The simulation results show that the proposed strategy is superior to other baseline strategies in terms of the total cost, delay, energy consumption, migration cost, and can be well adapted to different preferences and the dynamic network environment.