Vodcm: Value-Optimized Distributed Caching Mechanism for Containerized Aigc Services in Edge-Cloud Environments
Mingyuan Wang, Shihao Shen, Chao Qiu, Xiaofei Wang, Tao Luo, Cheng Zhang · 2025
With the rise of AI-Generated Content (AIGC) services, deployments within edge-cloud environments are becoming increasingly prevalent. Containerization offers resource isolation, lightweight deployment, and portability, making it a suitable technology for AIGC services. However, deploying AIGC services often requires large container images, leading to high deployment latency and bandwidth consumption. Based on real-world trace analysis showing the long-tail effect, where a few popular images account for the majority of requests, there is strong potential for optimizing caching mechanisms. This pattern can result in frequent cache misses and increased bandwidth consumption, especially under heavy load. In this paper, we propose a ValueOptimized Distributed Caching Mechanism (VODCM), which dynamically optimizes caching policies through a value-driven framework combined with deep reinforcement learning (DRL). VODCM prioritizes high-value images based on access frequency, layer size, and network latency, significantly improving cache hit rates and reducing network overhead. Preliminary evaluations show that VODCM enhances cache efficiency and reduces network and resource demands, offering an effective solution for AIGC image management in edge-cloud environments.