Co-Optimization of Partial Offloading and Resource Allocation for Multi-User Tasks in Vehicular Edge Networks

• Published in: IEEE transactions on parallel and distributed systems Vol. 36; no. 12; pp. 2537 - 2548
• Main Authors: Cao, Dun, Huang, Shirui, Gu, Ning, Alqahtani, Fayez, Sherratt, R. Simon, Wang, Jin
• Format: Journal Article
• Language: English
• Published: IEEE 01.12.2025
• Subjects: Collaboration; Costs; Delays; Edge computing; Energy consumption; Heuristic algorithms; Optimization; partial offloading; resource allocation; Resource management; Servers; Training; unequal splitting; Vehicle dynamics
• ISSN: 1045-9219; 1558-2183
• DOI: 10.1109/TPDS.2025.3571470

Mobile Edge Computing (MEC) effectively alleviates the pressure on limited in-vehicle computing resources and energy supply caused by computation-intensive vehicular applications. However, the uneven spatial distribution of users leads to load imbalance among adjacent MEC servers, significantly increase the latency and energy consumption costs for vehicles. Therefore, achieving optimal configuration of available computing resources in MEC servers to accomplish the goal of low-latency and low-energy task offloading has become a critical issue to address. To tackle this problem, this study proposes a Multi-RSU Load Balancing (MRLB) strategy based on multi-hop network technology. This strategy dynamically allocates computing tasks to neighboring RSU server clusters with available computing resources through task segmentation and computation offloading mechanisms. Meanwhile, adaptive resource allocation strategies are implemented based on task quantity and task scale characteristics. Specifically, this study designs a multi-RSU collaborative offloading algorithm based on Deep Deterministic Policy Gradient (DDPG) to solve the optimal offloading decision. Additionally, by integrating the Lagrange multiplier method and Sequential Quadratic Programming (SQP) algorithm, the joint optimization of imbalanced task segmentation decisions and optimal CPU frequency allocation decisions for RSU servers is achieved. Experimental results demonstrate that the proposed method can achieve efficient multi-RSU resource allocation and ensure coordinated optimization of both system latency and energy consumption costs across diverse device conditions and varying network scenarios, particularly in load-imbalanced situations.