Joint Model Caching and Resource Allocation in Generative AI-Enabled Wireless Edge Networks

TL;DR

This paper proposes a reinforcement learning approach to optimize model caching and resource allocation in wireless edge networks for generative AI, balancing quality and latency amid dynamic conditions.

Contribution

It introduces a DDPG-based method for joint model caching and resource allocation, addressing challenges of deploying large GenAI models at the edge.

Findings

Higher model hit ratio with DDPG

Lower latency in AIGC services

Superior performance over benchmark solutions

Abstract

With the rapid advancement of artificial intelligence (AI), generative AI (GenAI) has emerged as a transformative tool, enabling customized and personalized AI-generated content (AIGC) services. However, GenAI models with billions of parameters require substantial memory capacity and computational power for deployment and execution, presenting significant challenges to resource-limited edge networks. In this paper, we address the joint model caching and resource allocation problem in GenAI-enabled wireless edge networks. Our objective is to balance the trade-off between delivering high-quality AIGC and minimizing the delay in AIGC service provisioning. To tackle this problem, we employ a deep deterministic policy gradient (DDPG)-based reinforcement learning approach, capable of efficiently determining optimal model caching and resource allocation decisions for AIGC services in response to user mobility and time-varying channel conditions. Numerical results demonstrate that DDPG achieves a higher model hit ratio and provides superior-quality, lower-latency AIGC services compared to other benchmark solutions.