Sparkle: Optimizing the Serverless AIGC Deployment over Crowdsourced Edge Environments

The rise of Artificial Intelligence Generated Content (AIGC) applications demands scalable, cost-effective, and adaptable infrastructure. Crowdsourced edge networks, which utilize idle resources from third-party devices, offer low-cost environment essential for AIGC. This decentralized approach reduces capital expenditures and carbon footprints but faces challenges such as hardware volatility, where servers can unpredictably join or leave the network. The dynamic availability of servers leads to frequent service redeployments, necessitating efficient deployment procedure. In this paper, we propose a novel approach to deploying serverless AIGC applications, termed Sparkle. This method optimizes the deployment process by leveraging file-level granularity in image management and utilizing distributed image pulling and caching across edge networks. Experimental results from popular AIGC models demonstrate that Sparkle achieves up to 3.5x faster deployment times and a 28% reduction in registry storage space, outperforming current state-of-the-art technologies.