A Resource Management Strategy for Fluid Equilibrium in Edge-Cloud Market Supporting AIGC Services

作者：Xiaofei Wang, Chenxuan Hou, Chao Qiu, Xiaoxu Ren, Zehui Xiong, Haipeng Yao, Dusit Niyato · 发表于：IEEE Transactions on Services Computing · 年份：2025 · DOI：10.1109/tsc.2025.3583154 · 被引用次数：5 · 研究领域：Big Data and Business Intelligence、Customer churn and segmentation、Impact of AI and Big Data on Business and Society

The escalating demands for Artificial Intelligence-generated content (AIGC) services greatly require computing resources. The edge-cloud market offers an effective solution for AIGC services by integrating, managing, and trading distributed computing resources. Within this novel service market, participants contribute idle resources to support AIGC services to earn income, creating a more flexible market environment. Meanwhile, the generation quality and computing resource requirements of AIGC services are related to input prompts. Therefore, this relationship introduces new challenges, such asthe information uncertainty in input prompts, the inability to model resource continuity, and high-dimensional complexity for optimization.In this paper, we propose a resource fluid equilibrium management strategy for supporting AIGC services within edge-cloud market, termedFluE. To address the challenge of information uncertainty in user prompts, we measure the content value of AIGC prompts by information entropy and introduce a redundancy reduction approach to focus on meaningful information in prompts. To tackle the challenge of the inability to model the continuity provision of computing resources, we utilize the fluid model to ensure seamless resource provision and facilitate a more balanced management of computing resources. To address the challenge of high-dimensional complexity of strategy optimization, we develop a diffusion-based algorithm namedReDiffto reconstruct the target ...