2025/04/23 by Weixi Li, Rongzuo Guo, Li, Weixi +5
Business, Management and Accounting · Computer Science · Engineering · #Business Process Modeling and Analysis #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Robotics and Automated Systems #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.48550/arxiv.2504.16729
openalex publication_date 2025/04/23 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
With the rapid development of the Artificial Intelligence of Things (AIoT), mobile edge computing (MEC) becomes an essential technology underpinning AIoT applications. However, multi-angle resource constraints, multi-user task competition, and the complexity of task offloading decisions in dynamic MEC environments present new technical challenges. Therefore, a user-centric deep reinforcement learning (DRL) model splitting inference scheme is proposed to address the problem. This scheme combines model splitting inference technology and designs a UCMSMADDPG-based offloading algorithm to realize efficient model splitting inference responses in the dynamic MEC environment with multi-angle resource constraints. Specifically, we formulate a joint optimization problem that integrates resource allocation, server selection, and task offloading, aiming to minimize the weighted sum of task execution delay and energy consumption. We also introduce a user-server co-selection algorithm to address the selection issue between users and servers. Furthermore, we design an algorithm centered on user pre-decision to coordinate the outputs of continuous and discrete hybrid decisions, and introduce a priority sampling mechanism based on reward-error trade-off to optimize the experience replay mechanism of the network. Simulation results show that the proposed UCMSMADDPG-based offloading algorithm demonstrates superior overall performance compared with other benchmark algorithms in dynamic environments.