λ-trim: Optimizing Function Initialization in Serverless Applications With Cost-driven Debloating

In this paper, we focus on an often-overlooked component of serverless application cold starts: monetary costs and Function Initialization.Traditionally considered the user's responsibility, Function Initialization is billable and accounts for more than 50% of the monetary cost associated with cold starts in real-world machine-learning applications.We introduce 𝜆-trim, a system that optimizes Python serverless applications by eliminating redundant code while maintaining correctness.To maximize cost savings, 𝜆-trim leverages the typical serverless pricing model to prioritize modules that significantly impact latency and memory usage.𝜆-trim features an automated pipeline comprising a static analyzer, a profiler specialized for the serverless pricing model, and a debloater.The optimized application can be directly deployed on serverless platforms, leading to substantial reductions in both latency and cost for cold starts.