Performance Evaluation of Serverless Edge Computing for Machine Learning Applications

TL;DR

This paper provides a comprehensive performance analysis of serverless edge computing systems for machine learning applications, evaluating popular frameworks and workloads to identify challenges and opportunities.

Contribution

It offers the first detailed performance evaluation of serverless edge computing frameworks like Kubeless, OpenFaaS, Fission, and funcX for ML workloads, highlighting current challenges.

Findings

Performance varies across frameworks and workloads

Identifies bottlenecks in concurrency and resource management

Suggests best practices for deploying ML at the edge

Abstract

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.