Joint Model Assignment and Resource Allocation for Cost-Effective Mobile Generative Services

TL;DR

This paper proposes an edge-enabled system for assigning generative AI models and resources to mobile users, improving content quality and reducing latency through probabilistic model assignment and adaptive resource allocation.

Contribution

It introduces a novel probabilistic model assignment approach and a heuristic algorithm for dynamic resource allocation in edge-based AIGC services.

Findings

Content quality improved by up to 4.7%.

Response delay reduced by up to 39.1%.

Effective system for mobile AIGC services.

Abstract

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.