Universität Wien FIND

052515 VU Patterns and Artificial Intelligence for Cloud and Cloud-Edge Continuum (2022W)

Prüfungsimmanente Lehrveranstaltung


Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").


max. 25 Teilnehmer*innen
Sprache: Englisch


Termine (iCal) - nächster Termin ist mit N markiert

Please check Moodle for chosen lecture dates.

Dienstag 18.10. 16:45 - 18:15 Seminarraum 12, Währinger Straße 29 2.OG
Dienstag 08.11. 11:30 - 14:45 Seminarraum 11, Währinger Straße 29 2.OG
Mittwoch 09.11. 18:30 - 21:30 Seminarraum 9, Währinger Straße 29 2.OG
Donnerstag 10.11. 09:45 - 13:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Donnerstag 10.11. 18:30 - 21:30 Seminarraum 9, Währinger Straße 29 2.OG
Dienstag 15.11. 11:30 - 14:45 Seminarraum 11, Währinger Straße 29 2.OG
Mittwoch 16.11. 18:30 - 21:30 Seminarraum 9, Währinger Straße 29 2.OG
Donnerstag 17.11. 09:45 - 13:00 PC-Unterrichtsraum 2, Währinger Straße 29 1.OG
Donnerstag 17.11. 15:00 - 18:15 Seminarraum 9, Währinger Straße 29 2.OG


Ziele, Inhalte und Methode der Lehrveranstaltung

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)

Art der Leistungskontrolle und erlaubte Hilfsmittel

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

Mindestanforderungen und Beurteilungsmaßstab

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)


Course materials and accompanying literature.


See course materials on Moodle.

Zuordnung im Vorlesungsverzeichnis

Module: AT-PC

Letzte Änderung: Mo 31.10.2022 05:08