Mobility-Aware Deep Reinforcement Learning With Seq2seq Mobility Prediction for Offloading and Allocation in Edge Computing

• Published in: IEEE transactions on mobile computing Vol. 23; no. 6; pp. 6803 - 6819
• Main Authors: Wu, Chao-Lun, Chiu, Te-Chuan, Wang, Chih-Yu, Pang, Ai-Chun
• Format: Magazine Article
• Language: English
• Published: Los Alamitos IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Computational modeling; Deep learning; Deep reinforcement learning; Edge computing; Mobile computing; mobility; offloading decision; Prediction algorithms; Prediction models; Predictive models; Resource allocation; Resource management; seq2seq model; Servers; virtual machine migration
• ISSN: 1536-1233; 1558-0660
• DOI: 10.1109/TMC.2023.3328996

Mobile/multi-access edge computing (MEC) is 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 decision and resource allocation need to be jointly handled to optimize the service provision efficiency 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 in the MEC system. M-DRL is composed of two parts: glimpse , a seq2seq model customized for mobility prediction to predict a sequence of locations just like a "glimpse" of the future, and a DRL specialized in supporting offloading decisions and resource allocation in MEC. By integrating the proposed DRL and glimpse mobility prediction model, the proposed M-DRL framework is optimized to handle the MEC service provision with average 70% performance improvements.