Configuring Dynamic Multi-Stage Serverless Pipelines for Video Processing with Minimal Profiling Overhead

Serverless computing has become a promising paradigm for video processing workflows, offering simplified deployment and flexible management of business logic. However, the dynamic, multi-stage nature of video processing pipelines poses significant challenges for traditional serverless resource management, particularly in efficiently modeling optimal configurations and adapting to rapidly evolving pipeline structures. To address this challenge, we propose ConfigNavigator, a video pipeline resource tuning framework capable of adapting to dynamic inputs and pipeline structures with minimal overhead. In the offline phase, ConfigNavigator models function execution time distributions at the fundamental operation level and leverages graph theory to decompose complex video processing pipelines, thereby obtaining optimal configurations with minimal overhead. In the online phase, it dynamically adjusts function configurations on critical paths through real-time performance feedback, ensuring pipeline performance stability across varying workloads. We evaluate ConfigNavigator using real video streams on the commercial serverless platform AWS Lambda. Compared to state-of-the-art baselines, ConfigNavigator reduces configuration search time by 94.11% while decreasing end-to-end pipeline processing time by 13.97%.