Cold Start Influencing Factors in Function as a Service

Abstract

Function as a Service (FaaS) is a young and rapidly evolving cloud paradigm. Due to its hardware abstraction, inherent virtualization problems come into play and need an assessment from the FaaS point of view. Especially avoidance of idling and scaling on demand cause a lot of container starts and as a consequence a lot of cold starts for FaaS users. The aim of this paper is to address the cold start problem in a benchmark and investigate influential factors on the duration of the perceived cold start. We conducted a benchmark on AWS Lambda and Microsoft Azure Functions with 49500 cloud function executions. Formulated as hypotheses, the influence of the chosen programming language, platform, memory size for the cloud function, and size of the deployed artifact are the dimensions of our benchmark. Cold starts on the platform as well as the cold starts for users were measured and compared to each other. Our results show that there is an enormous difference for the overhead the user perceives compared to the billed duration. In our benchmark, the average cold start overheads on the user's side ranged from 300ms to 24s for the chosen configurations.