论文标题
机器学习应用程序的无服务器边缘计算的性能评估
Performance Evaluation of Serverless Edge Computing for Machine Learning Applications
论文作者
论文摘要
智能医疗保健，自动驾驶汽车和智能城市等下一代技术需要新的方法来应对物联网（IoT）设备生成的网络流量，以及有效的编程模型来部署机器学习技术。 Serverless Edge Computing是一种从最近的两种技术，即Edge Computing和无服务器计算的集成中的新兴计算范式，可以解决这些挑战。但是，几乎没有工作来探索这种技术的能力和性能。在本文中，提出了使用流行的开源框架对无服务器边缘计算系统进行的全面性能分析，即，kubeless，openfaas，fission和funcx。这些实验考虑了不同的编程语言，工作负载和并发用户的数量。机器学习工作负载已用于评估系统在不同的工作条件下的性能，以提供最佳实践的见解。评估结果揭示了无服务器边缘计算和开放研究机会中的一些当前挑战，用于机器学习应用程序。
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.
