Using component ensembles for modeling autonomic component collaboration in smart farming

Smart systems have become key solutions for many application areas including autonomous farming. The trend we can see now in the smart systems is that they shift from single isolated autonomic and self-adaptive components to larger ecosystems of heavily cooperating components. This increases the reliability and often the cost-effectiveness of the system by replacing one big costly device with a number of smaller and cheaper ones. In this paper, we demonstrate the effect of synergistic collaboration among autonomic components in the domain of smart farming---in particular, the use-case we employ in the demonstration stems from the AFarCloud EU project. We exploit the concept of autonomic component ensembles to describe situation-dependent collaboration groups (so called ensembles). The paper shows how the autonomic component ensembles can easily capture complex collaboration rules and how they can include both controllable autonomic components (i.e. drones) and non-controllable environment agents (flocks of birds in our case). As part of the demonstration, we provide an open-source implementation that covers both the specification of the autonomic components and ensembles of the use case, and the discrete event simulation and real-time visualization of the use case. We believe this is useful not only to demonstrate the effectiveness of architectures of collaborative autonomic components for dealing with real-life tasks, but also to build further experiments in the domain.This is the authors' version of the paper: P. Hnětynka, T. Bureš, I. Gerostathopoulos, J. Pacovský: Using Component Ensembles for Modeling Autonomic Component Collaboration in Smart Farming, in Proceedings of SEAMS 2020, Seoul, Korea, 2020. The final published version can be found at https://doi.org/10.1145/3387939.339159

Architectural Optimization for Confidentiality Under Structural Uncertainty

More and more connected systems gather and exchange data. This allows building smarter, more efficient and overall better systems. However, the exchange of data also leads to questions regarding the confidentiality of these systems. Design notions such as Security by Design or Privacy by Design help to build secure and confidential systems by considering confidentiality already at the design-time. During the design-time, different analyses can support the architect. However, essential properties that impact confidentiality, such as the deployment, might be unknown during the design-time, leading to structural uncertainty about the architecture and its confidentiality. Structural uncertainty in the software architecture represents unknown properties about the structure of the software architecture. This can be, for instance, the deployment or the actual implementation of a component. For handling this uncertainty, we combine a design space exploration and optimization approach with a dataflow-based confidentiality analysis. This helps to estimate the confidentiality of an architecture under structural uncertainty. We evaluated our approach on four application examples. The results indicate a high accuracy regarding the found confidentiality violations

Benchmarks for End-to-End Microservices Testing

Testing microservice systems involves a large amount of planning and problem-solving. The difficulty of testing microservice systems increases as the size and structure of such systems become more complex. To help the microservice community and simplify experiments with testing and traffic simulation, we created a test benchmark containing full functional testing coverage for two well-established open-source microservice systems. Through our benchmark design, we aimed to demonstrate ways to overcome certain challenges and find effective strategies when testing microservices. In addition, to demonstrate our benchmark use, we conducted a case study to identify the best approaches to take to validate a full coverage of tests using service-dependency graph discovery and business process discovery using tracing.Comment: 7 page

Visualizing Microservice Architecture in the Dynamic Perspective : A Systematic Mapping Study

As microservices become more popular, more drawbacks become apparent to developers. One issue that many teams face today is the failure to visualize the entire system architecture holistically. Without a full view of the system, the architecture can become convoluted as teams add and subtract from their system without reconciling their changes. One established practice to determine a view on the entire system involves dynamic analysis of microservice interaction and dependencies. In this mapping study, we investigate dynamic analysis as a way to visualize system architecture. Capturing the architectural view with dynamic analysis has the ability to build the system and then show its behavior at run-time. We identify dynamic analysis techniques, the corresponding tools, and the models that these practices can generate. The findings of this study are relevant to developers of decentralized systems looking for a way to visualize their system architecture in a dynamic perspective.publishedVersionPeer reviewe

Use Cases in Dataflow-Based Privacy and Trust Modeling and Analysis in Industry 4.0 Systems

Fostering efficiency of distributed supply chains in the Industry 4.0 often bases on IoT-data analysis and by means of lean- and shop oor-management. However, trust by preserving privacy is a precondition: Competing factories will not share data, if, e.g., the analysis of the data will reveal business relevant information to competitors. Our approach is enforcing privacy policies in Industry 4.0 supply chains. These are highly dynamic and therefore not manageable by \u27traditional\u27 rights-management approaches as we will stretch in a literature analysis. To enforce privacy, we analyze two industrial settings and derive general requirements: (1) Lean- and shop oor-management and (2) factory access control, both common in Industry 4.0 supply chains. We further propose a reference architecture for Industry 4.0 supply chains. We introduce the combination of Palladio Component Model (PCM) [23] and Ensembles [4] in order to analyze and enforce privacy policies in highly dynamic environments. Our novel approach paves way for data sharing and global data analyzes in highly dynamic Industry 4.0 supply chains. It is an important step for efficiency of these supply chains and therefore one important cornerstone for the success of Industry 4.0

Constraint-based Generation of Connectors

Abstract. In this paper we discuss the a typical use-case of connector usage in component-based systems. We show how the connectors are refined during application development and investigate a way how to automatically generate connectors with respect to style of interaction and component distribution.