EdgeAIGC: Model caching and resource allocation for edge artificial intelligence generated content

With the rapid development of generative artificial intelligence technology, the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs, which restrict user-side in-situ Artificial Intelligence Generated Content (AIGC) service requests. To this end, we propose the Edge Artificial Intelligence Generated Content (EdgeAIGC) framework, which can effectively address the challenges of cloud computing by implementing in-situ processing of services close to the data source through edge computing. However, AIGC models usually have a large parameter scale and complex computing requirements, which poses a huge challenge to the storage and computing resources of edge devices. This paper focuses on the edge intelligence model caching and resource allocation problems in the EdgeAIGC framework, aiming to improve the cache hit rate and resource utilization of edge devices for models by optimizing the model caching strategy and resource allocation scheme, and realize in-situ AIGC service processing. With the optimization objectives of minimizing service request response time and execution cost in resource-constrained environments, we employ the Twin Delayed Deep Deterministic Policy Gradient algorithm for optimization. Experimental results show that, compared with other methods, our model caching and resource allocation strategies can effectively improve the cache hit rate by at least 41.06% and reduce the response cost as well.