Distributed Two-Tier Cache Optimization in Metaverse Scenarios Combining MADDPG and GCN

The rapid emergence of the Metaverse requires higher network throughput and lower latency to deliver immersive and responsive virtual experiences. Traditional centralized data processing approaches are constrained by limited computational and bandwidth resources when handling large-scale user data. A Cloud-Edge-End transmission architecture is proposed in this study, tailored for Metaverse scenarios to optimize resource allocation, minimize latency, and enhance rendering efficiency. A real-time trajectory segment prediction scheme (FDK) was developed, which combines FastDTW with K-means by leveraging user behavior trajectories to determine subscene popularity and store them on GPU servers, thereby reducing user wait time. A two-tier cache optimization scheme (MAE2C) is also proposed, incorporating GCN for subscene feature identification. GPU servers employ the MADDPG strategy to cache popular subscenes, while edge servers utilize DDPG to cache missed scenes. This approach effectively reduces cloud access and cache replacement frequency. Simulation results demonstrate that the subscene cache hit rate of the MAE2C scheme significantly outperforms existing methods across various cache capacities, with a 6.9% reduction in cache replacement frequency. This research provides effective technical support for Metaverse scene rendering and offers insights into the development of generative Metaverse systems.