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Collaborative Multi-task Offloading in Multi-edge System for AI-Generated Content Service
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DOI：10.1016/j.comnet.2025.111979.png)
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Abstract
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As artificial intelligence-generated content (AIGC) services become increasingly prevalent in edge networks, the demand for rapid and efficient processing in latency-sensitive applications continues to grow. Traditional task offloading strategies often struggle to coordinate heterogeneous resources, such as GPU and TPU clusters, resulting in imbalanced load distribution and underutilization of specialized accelerators. To overcome these limitations, we propose the adaptive multi-edge load balancing optimization (AMBO) algorithm, designed to optimize collaborative task scheduling among edge servers. AMBO utilizes an online reinforcement learning approach, decomposing the task offloading process into edge server selection and load balancing functions, which enables intelligent scheduling across nodes with varying computational capacities. Furthermore, by integrating the dueling Deep Q-Network (DQN) framework, AMBO enhances decision-making accuracy and stability in dynamic edge environments. Extensive experimental results demonstrate that AMBO significantly improves task offloading efficiency, reducing task completion time by 79.04% and achieving a task completion rate of 99.89%. These results highlight the algorithm’s strong adaptability and effectiveness in heterogeneous edge computing scenarios, making it well-suited for supporting the next generation of latency-sensitive AIGC services.
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