Universität Wien
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052515 VU Patterns and Artificial Intelligence for Cloud and Cloud-Edge Continuum (2026S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Not all dates will be used. Details will be discussed during the kick-off lecture on March 4.

  • Wednesday 04.03. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
  • Wednesday 18.03. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
  • Wednesday 25.03. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
  • Wednesday 15.04. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
  • Wednesday 22.04. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
  • Wednesday 29.04. 13:15 - 14:45 Hörsaal 3, Währinger Straße 29 3.OG
  • Monday 01.06. 18:30 - 21:30 Seminarraum 7, Währinger Straße 29 1.OG
  • Tuesday 02.06. 13:15 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Tuesday 02.06. 16:45 - 20:00 Seminarraum 7, Währinger Straße 29 1.OG
  • Wednesday 03.06. 13:15 - 16:30 Seminarraum 4, Währinger Straße 29 1.UG
  • Friday 05.06. 16:45 - 20:00 Seminarraum 7, Währinger Straße 29 1.OG

Information

Aims, contents and method of the course

Cloud and Edge portability and interoperability gaps arise (among many reasons) when the semantics of resources, services, and sensors' APIs are not shared. Techniques borrowed from Pattern-based Software Engineering and AI (both Semantic and Machine Learning) areas can help in gaining a shared, machine-readable description of Cloud-Edge resources and Services, and of the Distributed Applications' Architecture. Thus enabling (semi-)automated discovery, matchmaking, selection, brokering, orchestration, interoperability, and composition of Cloud Services among multiple Clouds, and seamless and dynamically adaptive deployment of application components, on multiple Cloud and Cloud-Edge platforms.

This course offers a comprehensive and up-to-date overview of the most important methodologies, technologies, and standards related to the Cloud and Cloud-Edge continuum, with specific deepening to the application of Patterns and Artificial Intelligence techniques, to support Composition, Orchestration, and dynamic / adaptive deployment and reconfiguration of Services and Resources in a Multi-Cloud and Edge Interoperable scenario. Additionally, recent developments on supporting distributed Artificial Intelligence / Machine Learning services on the Cloud-Edge continuum (e.g., Federated Learning, Edge Intelligence) will be covered in the course, with a focus on systems learning from the Internet of Things data.

The techniques will be illustrated by means of concrete use cases and scenarios, based on concrete platforms (such as AWS, AZURE, Google, IBM, etc.). Practical exercises will include the pattern-based design of Cloud and Cloud-edge architectures, utilizing concrete services and design tools and languages such as Tosca, Terraform, Cloud Formation, etc.

Topics covered include
- Methods, Technologies, and Standards for Portable and Interoperable Cloud Programming
- Cloud Services Brokering
- Cloud and Cloud-Edge Patterns (Architectural, Computational, Deployment)
- Semantic representation of Cloud Services and Patterns for Cloud Portability and Interoperability
- Cloud Orchestrators (e.g., Ansible, Puppet, Chef, Kubernetes)
- Containers (Dockers) and MicroServices for portability and interoperability
- Machine Learning approaches for resource selection and placement on the Cloud-Edge continuum
- Architectures and Enabling technologies for Edge Intelligence (e.g., Centralized, Decentralized, Federated Learning)

Assessment and permitted materials

There will not be a traditional exam. The grade will be given based on the project work.

Minimum requirements and assessment criteria

Prerequisite knowledge: Programming, Software Engineering, basic notions of Cloud Computing

Grading Scheme:
>= 88% --> Sehr Gut (1)
>= 75% --> Gut (2)
>= 62% --> Befriedigend (3)
>= 50% --> Genügend (4)
< 50% --> Nicht Genügend (5)

Examination topics

Course materials and accompanying literature.

Reading list

See course materials on Moodle.

Association in the course directory

Module: AT-PC

Last modified: Mo 08.06.2026 11:06