Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services
This work addresses cost-effective mobile generative services for users and providers, but it is incremental as it builds on existing edge computing and resource allocation methods.
The paper tackles the challenge of supporting mobile users with AI-generated content services by designing an edge-enabled system that assigns models and allocates resources based on prompt categories, resulting in up to 4.7% improvement in content quality and up to 39.1% reduction in response delay compared to benchmarks.
Artificial Intelligence Generated Content (AIGC) services can efficiently satisfy user-specified content creation demands, but the high computational requirements pose various challenges to supporting mobile users at scale. In this paper, we present our design of an edge-enabled AIGC service provisioning system to properly assign computing tasks of generative models to edge servers, thereby improving overall user experience and reducing content generation latency. Specifically, once the edge server receives user requested task prompts, it dynamically assigns appropriate models and allocates computing resources based on features of each category of prompts. The generated contents are then delivered to users. The key to this system is a proposed probabilistic model assignment approach, which estimates the quality score of generated contents for each prompt based on category labels. Next, we introduce a heuristic algorithm that enables adaptive configuration of both generation steps and resource allocation, according to the various task requests received by each generative model on the edge.Simulation results demonstrate that the designed system can effectively enhance the quality of generated content by up to 4.7% while reducing response delay by up to 39.1% compared to benchmarks.