Background

Augmented reality, computer vision and other (e.g. network functions, Internet-of-Things (IoT)) use cases can be realised in edge computing environments with machine learning (ML) techniques. For realisation of the use cases, it has to be understood how data is collected, stored, processed, analysed, and visualised in big data systems. In order to provide services with low latency for end users, often utilisation of ML techniques has to be optimized. Also, software/service developers have to understand, how to develop and deploy ML models in edge computing environments. Therefore, architecture design of big data systems to edge computing environments may be challenging.

Findings

The contribution of this paper is reference architecture (RA) design of a big data system utilising ML techniques in edge computing environments. An earlier version of the RA has been extended based on 16 realised implementation architectures, which have been developed to edge/distributed computing environments. Also, deployment of architectural elements in different environments is described. Finally, a system view is provided of the software engineering aspects of ML model development and deployment.

Conclusions

The presented RA may facilitate concrete architecture design of use cases in edge computing environments. The value of RAs is reduction of development and maintenance costs of systems, reduction of risks, and facilitation of communication between different stakeholders.

中文翻译：

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将大数据系统的参考架构扩展到边缘计算环境中的机器学习

背景

可以使用机器学习（ML）技术在边缘计算环境中实现增强现实，计算机视觉和其他（例如，网络功能，物联网（IoT））用例。为了实现用例，必须了解如何在大数据系统中收集，存储，处理，分析和可视化数据。为了向终端用户提供低延迟的服务，通常必须优化ML技术的利用率。此外，软件/服务开发人员必须了解如何在边缘计算环境中开发和部署ML模型。因此，大数据系统到边缘计算环境的体系结构设计可能具有挑战性。

发现

本文的贡献是在边缘计算环境中利用ML技术的大数据系统的参考体系结构（RA）设计。RA的早期版本已基于16种已实现的实施体系结构进行了扩展，这些体系结构已开发到边缘/分布式计算环境。而且，描述了在不同环境中的架构元素的部署。最后，提供了关于ML模型开发和部署的软件工程方面的系统视图。

结论

提出的RA可以促进边缘计算环境中用例的具体架构设计。RA的价值在于减少系统的开发和维护成本，降低风险以及促进不同利益相关者之间的沟通。