Decentralized Systems (like blockchain-based systems) represent distributed systems that are controlled by multiple parties who make their own independent decisions. In this course, we cover fundamental theoretical aspects as well as up-to-date decentralized systems and connect theory with current practice. We thereby address fault tolerance, security & trust, as well as performance aspects. Furthermore, we address measurements, modeling and simulation of decentralized systems and applications like Bitcoin and Matrix.

Prior knowledge in Foundations of IT-Security and Computer Networks is recommended.

Amount of Work

Lecture: 15 x 2h = 30h
Weekly lecture preparation and follow-up: 15 x 2 x 2h = 60h
Exam preparation: 30h

120h = 4 ECTS

Learning Objectives

1. Theoretic Fundamentals
1. The student is able to recognize and distinguish distributed, federated, and decentralized systems.
2. The student understands consensus, consistency and coordination within the context of networked and decentralized systems.
3. The student understands the formally proven limits of fault tolerance and their underlying assumptions. This includes an understanding of the synchronous and asynchronous network model which underpin the respective proofs. The student also understands several models for fault tolerance, notably silent and noisy crash as well as byzantine fault tolerance within the context of decentralized and distributed systems.
4. The student knows various models for and levels of consistency. In particular, strictly ordered, causally ordered, partially ordered consistency as well as numerical and temporal relaxations thereof.
2. Modeling & Simulation
1. The student understands discrete event-based simulation as a scientific tool and is able to apply this concept properly to examine networked and decentralized systems.
2. The student understands the generation, manipulation, and evaluation of randomness and its relevance to simulation of networked and decentralized systems.
3. The student is able to statistically evaluate, visualize, and interpret the results of simulations.
3. Applications
1. The student has a fundamental understanding of blockchain-based cryptocurrencies (e.g. Bitcoin/Ethereum), decentralized communication systems like Matrix, and understands trust relations in distributed and decentralized systems.

The student is able to understand how the previously introduced theoretical foundations relate to networked and decentralized systems in practice.