The fast growth in the amount of connected devices with computing capabilities in the past years has enabled the emergence of a new computing layer at the Edge. Despite being resource-constrained if compared with cloud servers, they offer lower latencies than those achievable by Cloud computing. The combination of both Cloud and Edge computing paradigms can provide a suitable infrastructure for complex applications’ quality of service requirements that cannot easily be achieved with either of these paradigms alone. These requirements can be very different for each application, from achieving time sensitivity or assuring data privacy to storing and processing large amounts of data. Therefore, orchestrating these applications in the Cloud–Edge computing raises new challenges that need to be solved in order to fully take advantage of this layered infrastructure. This paper proposes an architecture that enables the dynamic orchestration of applications in the Cloud–Edge continuum. It focuses on the application’s quality of service by providing the scheduler with input that is commonly used by modern scheduling algorithms. The architecture uses a distributed scheduling approach that can be customized in a per-application basis, which ensures that it can scale properly even in setups with high number of nodes and complex scheduling algorithms. This architecture has been implemented on top of Kubernetes and evaluated in order to asses its viability to enable more complex scheduling algorithms that take into account the quality of service of applications.

The growing complexity of industrial automation demands the adoption of software engineering principles for improving the development process of control systems. This paper presents a methodological approach to the design and development of complex automation systems relying on model-driven engineering (MDE). A benefit of this approach is the integration of methods and techniques widespread within the automation discipline with modern MDE techniques guiding the designer through the development phases. A second advantage is to add flexibility enough to adapt the steps to the needs of the system under design. Finally, the architecture presented is prepared to be adapted to methodology extensions to cover other aspects of automation systems. The framework is based on domain models that are defined through the development phases using the terminology of the automation field. Using model transformations both documentation about system analysis and design and the skeleton of software units are automatically generated. A proof-of-concept tool has been developed that has been tested on the design of medium-complexity projects to assess the impact of its use with respect to project documentation and maintenance.

Digital Twin (DT) is one of the pillars of modern information technologies that plays an important role on industry digitalization. Its use varies very much with the domain but one of them, encouraged by integrated frameworks that cover the whole life cycle of manufacturing applications, is during Virtual Commissioning phase. This paper presents both a design and implementation methodology to easily test the control system reaction to process faults. In particular, the way to prepare the DT to support fault injection is analyzed. The proposal is tested through a simple but representative case study.

With cloud computing gaining momentum in industrial environments, the next step seems to move the computing infrastructure closer to the devices that collect the data at plant level. Fog computing is a paradigm that takes advantage of the fast response times of working relatively close to the plant, and the storage, processing and availability features of the cloud. Fog computing can be used to improve the controllability of automation processes by introducing a higher-level control loop: Fog-in-the-loop (FIL). FIL allows capturing data from the plant, processing it to extract information and feedback actions to the plant based on the processing results. Therefore, FIL applications are context-aware applications that require the deployment of distributed components and dynamic reconfiguration. In this context, we are involved in a project that aims at integrating a model-based, multipurpose Multi-Agent System (MAS) platform with a containerized application orchestrator to meet the requirements of Fog-in-the-loop applications. Specifically, this paper describes a custom scheduler for Kubernetes orchestrator that distributes the scheduling task among the processing nodes by means of the MAS. This new scheduling approach proved to be faster than the centralized scheduling approach used by the default scheduler of K8s.