Mobility-Aware Deep Reinforcement Learning with Glimpse Mobility Prediction in Edge Computing

Abstract

Mobile/multi-access edge computing (MEC) is therefore developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challenging issues in MEC is service provision with mobility consideration. It has been known that the offloading and migration decision need to be jointly handled to maximize the utility of networks within the latency constraints, which is challenging when users are in mobility. In this paper, we propose Mobility-Aware Deep Reinforcement Learning (M-DRL) framework for mobile service provision problems in the MEC system. M-DRL is composed of two parts: DRL specialized in supporting multiple users joint training, and glimpse, a seq2seq model customized for mobility prediction to predict a sequence of locations just like a “glimpse” of future. Through integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the service provision problem in MEC with acceptable computation complexity and near-optimal performance.