The course presents the most common programming techniques and methodologies used to create cloud-native applications, namely microservice-based applications, Kubernetes operators, Serverless, organization and management of large software projects, Git and workflow automation (CI/CD pipelines). In addition, the course will present an overview of techno-economic aspects such as drivers for cloud adoption, cost vs benefit analysis (including Capex vs Opex considerations), and agile development methodologies (agile, scrum, kanban, continuous improvement). Initial teaching labs focuses on individual technologies; more advanced labs focuses on the development of user-driven cloud-native software, which will be presented and discussed with the instructors.

- Knowledge of cloud-native programming paradigms, such as microservice-based applications, serverless, Kubernetes operators - Knowledge of most common methodologies for large cloud-native software projects, such as Git and workflow automation (CI/CD pipelines) - Capability to design a cloud-native software including techno-economic aspects, and to plan a migration-to-cloud strategy.

- Software engineering - Familiarity with common cloud computing technologies (e.g., Kubernetes, virtual machines)

- Microservice-based architectures and cloud-native development (0,6 cfu) - Kubernetes operators (1,2 cfu) - Serverless (0,6 cfu) - Git and workflow automation (0,6 cfu) - Agile methodology (0,6 cfu) - Strategies and economic considerations for the adoption of cloud computing technologies and services (0,6 cfu) - Group project (1,8 cfu)

The course includes 42 hours of traditional teaching, including small and technology-focused labs, which have to be completed by each single student. In addition, 18 hours are dedicated to a larger project, to be completed by groups of 3-4 students under the guidance of course instructors.

Lecture slides; Lab exercises; Video lectures (current year);

Slides; Esercitazioni di laboratorio; Video lezioni dell’anno corrente;

Modalita di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;

Exam: Compulsory oral exam; Group project;

... Expected learning outcome Knowledge of the topics presented in class and of the lab assignments. Capability to solve exercises that involve the technologies and methodologies presented in class. Capability to solve exercises that involve a more complex scenario including multiple technologies presented in class. Exam: rules, procedures Final exam is delivered through the discussion of the software project, and it can include additional questions on the theoretical topics presented in the lectures.

Gli studenti e le studentesse con disabilita o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unita Special Needs, al fine di permettere al/la docente la declinazione piu idonea in riferimento alla specifica tipologia di esame.