Experimental data can aid in gaining insights about a system operation, as well as determining critical aspects of a modelling or simulation process. In this paper, we analyze the data acquired from an extensive experimentation process in a serverless Function as a Service system (based on the open source Apache Openwhisk) that has been deployed across 3 available cloud/edge locations with different system setups. Thus, they can be used to model distribution of functions through multi-location aware scheduling mechanisms. The experiments include different traffic arrival rates, different setups for the FaaS system, as well as different configurations for the hardware and platform used. We analyse the acquired data for the three FaaS system setups and discuss their differences presenting interesting conclusions with relation to transient effects of the system, such as the effect on wait and execution time. We also demonstrate interesting trade-offs with relation to system setup and indicate a number of factors that can affect system performance and should be taken under consideration in modelling attempts of such systems.

翻译：实验数据有助于深入理解系统运行机制，并确定建模或仿真过程中的关键因素。本文基于开源Apache Openwhisk构建无服务器函数即服务（FaaS）系统，在三个可用云/边缘位置采用不同系统配置部署，通过对大规模实验过程获取的数据进行分析，这些数据可用于构建基于多位置感知调度机制的函数分布模型。实验涵盖不同流量到达率、FaaS系统配置方案以及硬件与平台配置的差异化设置。我们分析了三组FaaS系统配置下的实验数据，对比其差异，并就系统瞬态效应（如对等待时间与执行时间的影响）得出有趣结论。同时揭示了系统配置相关的关键权衡关系，指出影响系统性能的多项因素，这些因素在对此类系统进行建模时应予以充分考虑。