An Online Auction Approach to Computing Resource Allocation in Mobile AIGC Networks

We study resource allocation and task scheduling for mobile Artificial Intelligence Generated Content (AIGC) in a three-layer cloud-edge-device network. Escalating industry demand for computational resources presents significant challenges in resource allocation and optimization, particularly for edge-side AIGC, which faces high computational costs and requires advanced techniques for efficient model deployment on mobile devices. Optimal resource allocation in mobile AIGC networks is naturally formulated into a 0-1 ILP, which is proven NP-hard. We reformulate the problem into both its Comp-Exp and dual forms. Then we design an online auction framework OATS to optimize decisions on instances and time schedules, maximizing social welfare for the AIGC ecosystem. Our analysis demonstrates that OATS achieves high social welfare through appropriate bid acceptance and resource allocation. Simulation results corroborate the theoretical analysis, showcasing the efficacy of our online algorithms.