  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.
