The Night Shift: Understanding Performance Variability of Cloud Serverless Platforms

Abstract

Function-as-a-Service is a popular cloud programming model that supports developers by abstracting away most operational concerns with automatic deployment and scaling of applications. Due to the high level of abstraction, developers rely on the cloud platform to offer a consistent service level, as decreased performance leads to higher latency and higher cost given the pay-per-use model. In this paper, we measure performance variability of Google Cloud Functions over multiple months. Our results show that diurnal patterns can lead to performance differences of up to 15%, and that the frequency of unexpected cold starts increases threefold during the start of the week. This behavior can negatively impact researchers that conduct performance studies on cloud platforms and practitioners that run cloud applications.