Model-based tool for the design, configuration and deployment of data-intensive applications in hybrid environments: an industry 4.0 case study

Abstract

The fourth industrial revolution advocates the reformulation of industrial processes to achieve the end-to-end (provider-customer) digitalisation of the industrial sector. As is well known, the industrial environment is very complex, where legacy systems must interoperate and integrate with modern devices and sensors. Communication among them requires specific and costly developments, so architectures based on data sharing and services implementation are considered one of the most flexible and appropriate technological solutions to gradually achieve the desired horizontal and vertical integration of the value chain. The design and deployment of data-intensive applications is not straightforward, therefore this paper proposes a model-based tool to characterise the different elements to be configured in an application and to make its deployment easier by generating configuration, orchestration and deployment files and sending them to the corresponding nodes for their execution. In few words, this article highlights the advantages of distributed and data-centric architectures to face the challenge of integration and interoperability in data-intensive complex systems and presents the extension of the RAI4 metamodel proposed in Martínez et al. (2021) that now allows specifying how, containerised or not, and where, on the cloud, fog, edge or on-premise, each service can be hosted according to its functional and non-functional requirements, mainly issues related with real-time, security and cyber physical hardware dependencies. For the sake of comprehension, a pseudo-real use case addressed to pre-process and store pollution data from environmental sensors installed in a smart city is described in detail, including different deployment settings.