Claudia Canali

The Fog computing paradigm makes use of dispersed, diverse, and resource-limited devices located at the network edge to effectively implement Internet of Things (IoT) application services that demand low latency and substantial bandwidth. At the same time, the adoption of microservice-based architectures in the IoT domain is on the rise due to their ability to align with the swift evolution and deployment demands of highly dynamic IoT applications and to elastically scale to fulfill load demands. In complex environments like Fog federations, characterized by highly heterogeneous computing and networking resources, the effective allocation of microservices to available nodes, while ensuring compliance with required Quality of Service (QoS) constraints, represents a significant challenge. In this paper, we present the design and implementation of OptiFog, a comprehensive framework that enables users to model, simulate, and validate microservice placement solutions within a realistic testbed environment. Compared to state-of-the-art approaches, OptiFog offers developers a controlled environment for experimenting with placement solutions while providing the assurance that the resulting deployments will meet the targeted QoS requirements in real-world scenarios, specifically in terms of service execution time and energy consumption of Fog nodes. To demonstrate the feasibility of the proposed approach, we implemented and evaluated a representative use case, involving both sub-optimal and optimal microservice placement, and utilizing real-world microservices drawn from the IoT domain.

In recent years, despite the growth of initiatives aimed at improving the gender gap in STEM disciplines, the numberof girls choosing academic and professional paths in fields such as computer science (CS) remains limited. To counteract thisphenomenon, initiatives like the Digital Girls Summer Camp, which has been running for over ten years for girls about tomake university choices, offer practical and engaging experiences designed to spark interest in technological subjects.Through activities such as programming and video game design on advanced platforms like Unreal Engine, participantsacquire technical skills and confidence in their abilities, supported by female educators and mentors who serve as rolemodels. The camp follows a gradual learning approach, starting with simple concepts and progressing to complex projects,thus stimulating the participants’ interest. This study aims to analyze the effectiveness of the most recent editions of theinitiative by using a qualitative and quantitative approach to evaluate how the girls change their perception of theirtechnological abilities and interest in CS. Through the submission of pre- and post-camp questionnaires, it is possible to assessthe increase in interest in STEM careers, as well as analyze the perception of the figure of the computer scientist before andafter the camp experience. The work describes how participation in the camp has significantly contributed to shiftingperceptions toward computer science, showcasing how hands-on experience and exposure to positive role models cantransform the image of the computer scientist, inspiring participants to view it as an inclusive and dynamic profession.

The utilization of Web-based interactive analytical tools in the research field is on the rise. Languages like Python and R are becoming widely adopted for analysis due to their extensive libraries and - mainly in the Python case - thanks to a relatively smooth learning curve for data analysis purposes. Consequently, tools like Jupyter Notebook have become the de facto standard for data analysis. These tools offer numerous benefits, such as a browser-based interface, reproducible workflows, and integration of text, code, and results in one document, making them attractive even to non-computer scientists. However, they face challenges in sharing and real-time collaboration, which both the Jupyter community and commercial solutions are addressing. Based on feedback from our researchers and experiences in multidisciplinary fields, our research aimed to integrate the Jupyter Notebook into a broader online file storage system for an enriched research workflow. Specifically, we developed an open-source application for Nextcloud called NextPyter. The application integrates Jupyter Notebook within the Nextcloud platform using Docker technology, enhancing friendly utilization and real-time collaboration.

In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of data. Relying on a cloud computing approach may not represent the most suitable solution in these scenario due to the non-negligible latency between data sources and distant cloud data centers, which may represent an issue in cases involving real-time and latency-sensitive IoT applications. Placing certain tasks, such as preprocessing or data aggregation, in a layer of fog nodes close to sensors or end users may help to decrease the response time of IoT applications as well as the traffic towards the cloud data centers. However, the fog scenario is characterized by a much more complex and heterogeneous infrastructure compared to a cloud data center, where the computing nodes and the inter-node connecting are more homogeneous. As a consequence, the the problem of efficiently placing microservices over distributed fog nodes requires novel and efficient solutions. In this paper, we address this issue by proposing and comparing different heuristics for placing the application microservices over the nodes of a fog infrastructure. We test the performance of the proposed heuristics and their ability to minimize application response times and satisfy the Service Level Agreement across a wide set of operating conditions in order to understand which approach is performs the best depending on the IoT application scenario.

In the last few years, workforce with STEM (Science, Technology, Engineering and Math) competencies has proved to be crucial for countries' innovative capacity and global competitiveness. Yet women are vastly underrepresented in STEM and, in particular, in ICT and computer science fields, both among workers and degree holders: this gap hinders the possibility for ICT employment to be strengthened and for women to take advantage of career opportunities, thus perpetuating gender inequalities in these disciplines. To counteract these effects and attract girls towards ICT-related fields of study and careers, several initiatives have been organised all around the world, such as summer camps and dedicated extracurricular activities. However, these initiatives are usually not supported by proper evaluation tools allowing researchers and practitioners to understand the actual benefits of the carried-out activities on girls' competencies and future attitudes. In this paper, we propose an evaluation tool for extracurricular activities aimed at reducing the gender gap in ICT. The proposed tool aims at capturing both a quantitative and a qualitative evaluation, including an Implicit Association Test (IAT) along with a more traditional questionnaire consisting of thematic sections designed to analyse various aspects of the activities' impact on girls. The tool has been applied in the context of two summer camps related to national and international projects aimed at attracting girls towards computer science and STEM disciplines: the ‘Digital Girls’ project, organised since 2014 by the University of Modena and Reggio Emilia in collaboration with other universities and local institutions, and the “STEM for Future” Erasmus+ project. Based on the results obtained by the summer camp case studies, we discuss some critical elements that can hinder the efficacy of the evaluation tool, giving suggestions to overcome these potential issues.