  Next generation technologies such as smart healthcare, self-driving cars, and
smart cities require new approaches to deal with the network traffic generated
by the Internet of Things (IoT) devices, as well as efficient programming
models to deploy machine learning techniques. Serverless edge computing is an
emerging computing paradigm from the integration of two recent technologies,
edge computing and serverless computing, that can possibly address these
challenges. However, there is little work to explore the capability and
performance of such a technology. In this paper, a comprehensive performance
analysis of a serverless edge computing system using popular open-source
frameworks, namely, Kubeless, OpenFaaS, Fission, and funcX is presented. The
experiments considered different programming languages, workloads, and the
number of concurrent users. The machine learning workloads have been used to
evaluate the performance of the system under different working conditions to
provide insights into the best practices. The evaluation results revealed some
of the current challenges in serverless edge computing and open research
opportunities in this emerging technology for machine learning applications.
