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.

翻译：函数即服务是一种流行的云编程模型，它通过自动部署和扩展应用程序来抽象化大多数运维问题，从而为开发者提供支持。由于抽象化程度较高，开发者依赖云平台提供一致的服务水平，因为性能下降会导致延迟增加和按使用付费模型下的成本上升。在本文中，我们基于多个月的测量，评估了Google Cloud Functions的性能波动性。我们的结果显示，昼夜模式可能导致高达15%的性能差异，且在每周开始时，意外冷启动的频率增加了三倍。这种行为可能对在云平台上进行性能研究的科研人员和运行云应用程序的从业者产生负面影响。