Optimizing Cloud Function Configuration via Local Simulations

Abstract

Function as a Service (FaaS) - the reason why so many practitioners and researchers talk about Serverless Computing - claims to hide all operational concerns. The promise when using FaaS is that users only have to focus on the core business functionality in form of cloud functions. However, a few configuration options remain within the developer's responsibility. Most of the currently available cloud function offerings force the user to choose a memory or other resource setting and a timeout value. CPU is scaled based on the chosen options. At a first glance, this seems like an easy task, but the tradeoff between performance and cost has implications on the quality of service of a cloud function. Therefore, in this paper we present a local simulation approach for cloud functions and support developers in choosing a suitable configuration. The methodology we propose simulates the execution behavior of cloud functions locally, makes the cloud and local environment comparable and maps the local profiling data to a cloud platform. This reduces time during the development and enables developers to work with their familiar tools. This is especially helpful when implementing multi-threaded cloud functions.