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2022　Fault Tolerant Distributed Algorithms

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Academic unit or major
Graduate major in Computer Science
Instructor(s)
Bonnet Francois Pierre Andre  Defago Xavier 
Class Format
Lecture    (HyFlex)
Media-enhanced courses
Day/Period(Room No.)
Mon7-8(W831)  Thr7-8(W831)  
Group
-
Course number
CSC.T527
Credits
2
Academic year
2022
Offered quarter
3Q
Syllabus updated
2022/4/20
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

The course aims to develop a thorough understanding of fault-tolerance in distributed systems. Due to their nature, distributed systems are inherently vulnerable to failures if not designed properly. At any time, a subset of the processes in a distributed system may fail by crashing or could be compromised and behave in a treacherous way (e.g., Byzantine failures). It is hence essential to design distributed systems and applications in such a way that they can adequately cope with failures. The lecture will present focus on how to deal with these issues.

Student learning outcomes

By studying relevant methods and algorithms in details, the student will acquire a deep understanding of the issues at hand and the basic mechanisms to deal with such failures. Although the course will focus on the theory of such systems, it will also systematically draw links with practical applications, making it valuable to both theoreticians and practitioners.

Keywords

Distributed algorithms, message-passing, synchrony models, agreement, replication, fault-tolerance, Byzantine agreement, self-stabilization, blockchain, randomized algorithms

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Typical classes will alternate between slide-based presentations, interactive discussions, class exercises. Active contribution to class discussions is strongly encouraged.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction, models & definitions Revision of basic concepts of distributed algorithms (models, synchrony, causality)
Class 2 Synchronous consensus instructed in class.
Class 3 Asynchronous consensus, FLP impossibility proof instructed in class.
Class 4 Consensus in the presence of partial synchrony instructed in class.
Class 5 Asynchronous consensus with unreliable failure detectors instructed in class.
Class 6 Eventual leader election, Paxos instructed in class.
Class 7 Byzantine consensus (I) instructed in class.
Class 8 Byzantine consensus (II) instructed in class.
Class 9 Randomized consensus instructed in class.
Class 10 State-machine replication instructed in class.
Class 11 Distributed transactions, distributed ledger, blockchain mechanisms instructed in class.
Class 12 Self-stabilization (I) instructed in class.
Class 13 Self-stabilization (II) instructed in class.
Class 14 Q&A + final test instructed in class.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Course materials:
Slide copies, additional article copies, ...made available for download from the course webpage.

Reference books, course materials, etc.

Reference Books:
1. Michel Raynal, "Fault-tolerant message-passing distributed systems," Springer, 2018. https://www.springer.com/gp/book/9783319941400
2. Ajay Kshemkalyani, Mukesh Singhal, "Distributed computing: principles, algorithms, and systems," Cambridge Uni. Press, 2011.
3. Wan Fokkink, "Distributed algorithms: an intuitive approach ," MIT Press, 2013.
4. Vijay K. Garg, "Elements of distributed computing," IEEE, 2002.
5. Gerard Tel, "Introduction to distributed algorithms (2nd ed.)," Cambridge Univ. Press, 2000.
6. Shlomi Dolev, "Self-Stabilization," MIT Press, 2000. https://mitpress.mit.edu/books/self-stabilization
7. Karine Altisen, Stéphane Devismes, Swan Dubois, Franck Petit, "Introduction to Distributed Self-Stabilizing Algorithms", Morgan & Claypool, 2019.

Assessment criteria and methods

Homework assignments and contribution to class discussion, assignments, reports (60%); and examination (40%).

Examination will assess the understanding of basic concepts of fault-tolerant distributed algorithms (problems, algorithms, and methodology) and reasoning (correctness and complexity).

Related courses

  • CSC.T438 ： Distributed Algorithms
  • CSC.T524 ： Dependable Computing
  • MCS.T213 ： Introduction to Algorithms and Data Structures

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Required knowledge:
Prior to taking this course, the student must have previously acquired,
through lectures or self-study, background knowledge on basic concepts
of fault-free distributed algorithms such as taught in the following
course:
- CSC.T438 Distributed algorithm

Other

Related course:
In the field of fault-tolerant and dependable computing systems, this course is complementary with:
- CSC.T524 Dependable Computing
- CSC.T438 Distributed Algorithms

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