Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

2025-03-11

IEEE Transactions on Mobile Computing

AI Summary

To address service interruptions and inefficient resource allocation in multi-edge Internet of Vehicles (IoV) caused by high vehicle mobility and limited base station coverage, this paper proposes the SR-CL framework. Methodologically, it first decouples the mixed-integer nonlinear programming (MINLP) problem into a learnable service migration subproblem and an analytically tractable resource allocation subproblem; second, it designs an asynchronous-update Actor-Critic deep reinforcement learning algorithm to enable seamless service migration; third, it derives a real-time optimal resource allocation policy via convex optimization theory. Simulation results based on real-world vehicular trajectory data demonstrate that SR-CL achieves a 32% faster convergence rate and reduces end-to-end latency to 18.7 ms (mean), significantly outperforming state-of-the-art baselines. The framework thus enables efficient, dynamic, and quality-guaranteed collaborative optimization in highly mobile edge-IoV environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios.

Problem

• Maintain uninterrupted IoV services during high vehicle mobility.
• Optimize resource allocation during service migration in MEC systems.
• Enhance system performance using convex optimization and deep reinforcement learning.

Innovation

• Convex-optimization-enabled deep reinforcement learning
• Actor-critic-based asynchronous-update method
• Decoupled MINLP for service migration