Hierarchical Meta-Graph Reinforcement Learning for Collaborative GenAI Model Caching and Inference Scheduling
Abstract
Enabling collaborative generative AI (GenAI) inference at the network edge is challenging due to limited caching capacity, heterogeneous computing resources, and highly dynamic, latency-sensitive service demands. In this paper, we investigate the joint optimization of GenAI model caching, inference offloading, and resource allocation in a collaborative cloud–edge–end architecture. To address the strong coupling between long-term caching decisions and short-term scheduling dynamics, we propose a Hierarchical Meta-Graph Reinforcement Learning framework, termed HMGRL. Specifically, a heat-greedy model caching strategy is developed to capture time-varying model popularity and to reduce switching overhead on a slow timescale, while a graph-enhanced dueling deep reinforcement learning algorithm with prioritized experience replay enables topology-aware collaborative inference offloading and resource allocation on a fast timescale. Extensive simulations demonstrate that HMGRL consistently outperforms representative baselines in terms of system utility, cache and computing-resource utilization, convergence stability, and performance robustness. These results validate the effectiveness of the proposed hierarchical learning framework for practical GenAI applications at the network edge.