Prototype of a Scalable Monitoring Infrastructure for Industrial DevOps

The digitalization of the conventional, manufacturing industry poses great opportunities but also challenges. As industrial devices, machines, and plants become more intelligent and autonomous, an increasing challenge for business enterprises is to connect and integrate them. This leads to the fact that software plays an increasing role in the daily production processes. In particular, small and medium-sized enterprises have to take high risks caused by high investment costs and a lack of experience in the development of distributed software systems. Agile and iterative methods, such as those that have long been common in other fields of IT, can be a solution to that. Industrial DevOps is an approach to apply those methods and bridge the gap between the development and the operation of software in industrial production environments. To facilitate this, development and operation are considered as a coherent, cyclic, and continuous process. Therefore, Industrial DevOps requires a continuously monitoring of all different aspects within a production. In this thesis, we present an approach for a monitoring infrastructure for industrial production environments such as factories. It facilitates the integration of different types of sensors in order to make their measurements comparable. Automatically and in real time, recorded data is analyzed and visualized in a number of ways. Due to its microservice-based architecture, our monitoring infrastructure is designed to scale with the production size and to adapt to frequently changing requirements. This is supported by applying open source big data tools that have demonstrated their capabilities in similar challenges of Internet-scale systems. Moreover, to face the challenges of scalability and real-time data processing, our approach unites the principles of cloud and edge computing. We implemented a prototype that covers all aspects of the designed architecture. This prototype is deployed in a medium-sized enterprise, where we monitored the electrical power consumption of a server, to show the feasibility of our approach. Furthermore, our approach shows scalability characteristics even though it is not always achieved reliably. Therefore, we present possible solutions to increase the reliability.