Advanced Distributed Systems - Interaktive Studienpläne der TU Ilmenau
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| Modulinformationen zu Advanced Distributed Systems im Studiengang Master Data Science 2026 | |
|---|---|
| Modulnummer | 201197 |
| Prüfungsnummer | 2200875 |
| Fakultät | Fakultät für Informatik und Automatisierung |
| Fachgebietsnummer | 2255 (Verteilte Systeme und Betriebssysteme) |
| Modulverantwortliche(r) | Prof. Dr. Boris Koldehofe |
| Turnus | Wintersemester |
| Sprache | Englisch |
| Leistungspunkte | 5 |
| Präsenzstudium (h) | 45 |
| Selbststudium (h) | 105 |
| Verpflichtung | Wahlmodul |
| Abschluss | mündliche Prüfungsleistung, 30 Minuten |
| Details zum Abschluss | |
| Link zum Moodle-Kurs | https://moodle.tu-ilmenau.de/course/view.php?id=3845 |
| Lehrende | Prof. Dr. Boris Koldehofe |
| Anmeldemodalitäten für alternative PL oder SL | |
| max. Teilnehmerzahl | |
| Vorkenntnisse | Pre-knowledge on fundamental aspects of computer science obtained for instance in a BSc program, in particular data structures and algorithms, basic concepts of programming languages, basic knowledge on computer networks, computer architecture or distributed and operating system principles. |
| Lernergebnisse und erworbene Kompetenzen | At the end of the course, students can reproduce and explain concepts for dealing with dynamic and large-scale distributed systems. Students show a deep understanding of system concepts to ensure performance, robustness, and security of distributed applications. Students can explain the properties of specific concepts for autoscaling and securing distributed applications and demonstrate, analyze, and prove their behavior. Students are able to interpret and use different models and abstractions of advanced distributed systems and select appropriate mechanisms for dealing with highly heterogenous components from the continuum of resources comprising IoT devices, edge resources, cloud resources, and network components. The student can compare the suitability of algorithms and mechanisms for specific advanced distributed systems applications, reason about their limitations, and can relate the findings to particular use cases, e.g., the Internet of Things and scalable data analysis. |
| Inhalt | Nowadays, distributed systems are highly dynamic and often integrate many heterogeneous resources for computing communication and storage over a continuum of cloud data centers, edge data centers, user devices, sensors, and network components. In this course, students will study the principles behind current distributed technologies and modern architectures that support building highly scalable and robust distributed applications. The goal of the course is to develop an understanding of the principles behind advanced distributed systems technologies and programming concepts to build scalable, robust, and secure distributed systems applications. In particular, the lecture will cover the following topics: 1) Modern technologies and architectural concepts of distributed systems covering principles behind cloud, edge, fog, serverless and in-network computing 2) Systematic study of autoscaling concepts for distributed systems 3) Accelerating the performance of distributed systems with hardware accelerators and new system concepts, e.g., P4, DPDK, and RDMA 4) Advanced distributed programming concepts supporting scalable and robust distributed systems, e.g., asynchronous communication, distributed ledgers, distributed machine learning 5) Advanced security and privacy mechanisms, e.g., differential privacy, attribute-based encryption, distributed authentication and authorization architectures and federated learning |
| Medienformen und technische Anforderungen bei Lehr- und Abschlussleistungen in elektronischer Form | Slides Lecture Recording Exercise Assignments & Solutions Quizzes Blackboard Discussion |
| Literatur | The literature list provides pointers for complementary reading only and will be updated before the start of the course:
M. van Steen, A. S. Tanenbaum. Distributed Systems. Ed. 3.01. 2017. ISBN 978-1543057386. |
| Lehrevaluation | |