Forklift: Fitting Zygote Trees for Faster Package Initialization
Year: 2024
Type: conference-paper
Abstract: Fast cold start is critical for many serverless applications. For Python, startup is typically dominated by module initialization. In this work, we analyze the dependencies of 9,678 Python applications and create ReqBench, a new benchmark for stressing module initialization. Informed by our findings, we design Forklift, a new algorithm for training zygote trees based on invocation history. Each zygote pre-imports some modules and can be forked to create other zygotes or function instances. We integrate Forklift with OpenLambda, improving median invocation latency by about 5× while using only a modest memory footprint (<6 GB).
Authors: Yuanzhuo Yang, Kwangjong Choi, Keting Chen, Tyler Caraza-Harter
Institution: University of Wisconsin–Madison
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4404755772