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| Title | Performance Evaluation Of Serverless Edge Computing For Machine Learning Applications |
|---|---|
| ID_Doc | 41780 |
| Authors | Trieu Q.L.; Javadi B.; Basilakis J.; Toosi A.N. |
| Year | 2022 |
| Published | Proceedings - 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing, UCC 2022 |
| DOI | http://dx.doi.org/10.1109/UCC56403.2022.00025 |
| Abstract | Next generation technologies such as smart health-care, 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. © 2022 IEEE. |
| Author Keywords | Autoscaling.; Edge computing; Machine learning; Response time; Serverless computing |
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