Cold Start Latency in Serverless Computing: Snapshotting, Fork-Based Models, and the Firecracker MicroVM Approach

Abstract

Cold start latency is a persistent, largely unsolved problem in serverless computing. When a function hasn't run recently, the platform must boot a VM or container, initialize a runtime, load the application framework, and only then handle the request — a process that takes 100ms to several seconds depending on platform and runtime. For latency-sensitive workloads, that overhead disqualifies the platform. We examine the root causes, build a decomposition model, and evaluate three mitigation strategies: memory snapshotting, fork-based execution (REAP), and Firecracker snapshot restore. We benchmark all three across five production-representative workloads. Snapshot restore cuts median cold start from 145ms to 28ms. Fork-based models (REAP) push that to 12ms. We also derive a warm pool sizing formula and model copy-on-write memory behavior under concurrent load. Our results show that fork-based approaches lead on latency while snapshot restore offers stronger isolation — making the right choice workload-dependent.