Software-as-a-Service solutions are increasingly being adopted when developing software applications, as they are scalable, cost-effective, and facilitate rapid deployment while providing high availability and flexibility. However, the impact of Software-as-a-Service in terms of carbon emissions is not yet adequately addressed as a design concern, and most of the existing efforts revolve around measuring and containing the carbon impact after the deployment. Our work proposes a model-driven reasoning framework that integrates UML-based software architecture modeling with carbon-aware concerns. Architectural elements are supplemented with sustainability and performance properties of interest through a dedicated Domain Specific Language; then, a model-driven transformation generates a simulation model to evaluate multiple architectural designs according to their Software Carbon Intensity and performance metrics. The results guide decision-making by assessing and comparing the trade-offs between performance and carbon intensity for the analyzed designs. In this way, the reasoning framework provides an automated, tool-supported approach to designing environmentally responsible Software-as-a-Service applications.

Software-as-a-Service solutions are increasingly being adopted for delivering software applications, as they are scalable, cost-effective, and facilitate rapid deployment while providing high availability and flexibility. However, ensuring expected performance while tackling carbon emission concerns poses a significant challenge for the sustainable design and operation of software distributed over the cloud. The proposed research outline investigates Software-as-a-Service carbon impact throughout the main software development stages. As a first step, insights on the modeling and simulation of carbon-related metrics were derived from the lifecycle of a software system. The resulting knowledge led to the development of a reasoning framework enabling carbon awareness during the design phase of cloud-based software architectures. Future steps aim to integrate data visualization capabilities into the reasoning framework to understand the importance of the designed application features in the evaluation process and favor enhanced decision support. Furthermore, an adaptation framework is envisioned to handle uncertainty during the software runtime and keep the balance between carbon impact and performance under control. Together, these steps form a cohesive path toward carbon-aware Software-as-a-Service solutions design and operation. 

Business process management deals with the administration of the chains of events, activities, and decisions that add value to an organization. The ever-growing complexity and inner uncertainty of business-related operations call for a paradigm shift toward new technologies, such as digital twins, to coordinate and optimize the processes belonging to an organization. In this paper, we propose an extended version ofthe business process lifecycle that includes a Digital Twin of the Organization, in charge of consistently ensuring the process’s alignment with the required performance goals and facilitating recovery from failures. We also contribute to defining the model-driven components that transform the business process model, in BPMN notation, into a compatible process representation for the digital twin, enabling simulation capabilities and scenario analyses. Through a manufacturing use case, we demonstrate how the digital twin optimizes processes pre-implementation and facilitates rapid recovery and reconfiguration following critica lfailures, underscoring its potential to improve efficiency and reliability in process management.

As simulation applications become vital for understanding and predicting complex systems, analyzing data from repeated simulation runs is essential to gauge model uncertainty and identify optimal parameter settings. This paper presents a visualization tool for analyzing time series ensemble data generated by discrete-event simulations, focusing on feature importance within clustering results. The tool combines dimensionality reduction, clustering, and SHapley Additive exPlanations (SHAP) to highlight influential features and identify trends within clustered simulation data, advancing previous approaches focusing solely on visualization or clustering without analyzing specific feature contributions. By analyzing a manufacturing use case, we show how the visualization supports decision-makers by depicting the main features driving cluster formation and displaying time intervals critical to characterizing distinct system behaviors.

Digital Twin of the Organization (DTO) is a relatively new concept that emerged to help managers have a full understanding of their organization and realize their objectives. Indeed, DTO enables connecting all the elements of an organization virtually by providing monitoring, optimization, prediction, and other capabilities through continuous simulations. Creating a flexible and evolvable DTO that covers and supports the organization's business strategies is a complex and time-consuming task that requires engineering best practices. In this context, this paper presents and evaluates the EA Blueprint Pattern, which serves as an architectural reference for the development of a DTO by allowing for mapping well-known Enterprise Architecture concepts into software components defining the DTO software architecture. The evaluation is carried on by showing how to use the pattern for creating the DTO for a given organization. Then, a thorough discussion is conducted to analyze how the developed DTO should evolve to deal with vertical and horizontal integration. The lessons learned highlight the strengths and weaknesses along with practical implications for organizations that are eager to develop their DTO according to the EA Blueprint Pattern.

Robots are increasingly adopted in a wide range of unstructured and uncertain environments, where they are expected to keep quality properties such as efficiency, accuracy, and safety. To this end, robots need to be smart and continuously update their situation awareness. Self-adaptive systems pave the way for accomplishing this aim by enabling a robot to understand its surroundings and adapt to various scenarios in a systematic manner. However, some situations, e.g., adjusting adaptation rules, refining run-time models, narrowing a vast adaptation domain, and taking future scenarios into consideration, etc. may require the self-adaptive system to include additional specialized components. In this regard, this work proposes a novel approach combining the MAPE-K, adaptive controllers, and a Digital Twin of the robot to enable the managing system to be aware of new scenarios appearing at run-time and operate safely, accurately, and efficiently. A state-of-the-art robot model is employed to evaluate the suitability of the approach.

The advent of IoT opened up countless possibilities for the realization of smart devices, architectures and systems. In several circumstances, however, these components are designed to provide a good experience in terms of performance as well as usability, often choosing them over other features, such as energy efficiency. In this paper, a model-based architecture derived from the Collect-Organize Pattern for Self-Adaptation is presented through a use case involving a smart household, and a brief discussion on its applicability in a realistic scenario is provided.