Empirical Investigation of Factors influencing Function as a Service Performance in Different Cloud/Edge System Setups

TL;DR

This study empirically examines how various system configurations, traffic loads, and deployment locations impact the performance of serverless Function as a Service systems, providing insights for better modeling and optimization.

Contribution

It offers a comprehensive analysis of FaaS performance across different setups and locations, highlighting key factors influencing system behavior and transient effects.

Findings

Performance varies significantly with system setup and location.

Transient effects impact wait and execution times.

Trade-offs exist between system configuration choices.

Abstract

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.