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Table of Contents
Course 2.18.1: Distributed Algorithms for Networks (24h, 3ECTS)Responsible of the course: Pierre Fraigniaud (Directeur de Recherche CNRS, IRIF, Université de Paris) Schedule: Unless specified otherwise, during the first period (Sept. 13 to Nov. 19), the lectures will take place from 10:15 to 11:45 every Tuesday morning, room 1002. Lecturers Year 2021-2022
Scientific and pedagogical contentDistributed Computing is dedicated to the design and analysis of algorithms performed by a set of autonomous computing entities whose objective is the realization of a common task, in absence of any coordinator, like in, e.g., Internet, P2P systems, cloud computing, wireless networks, social networks, insect colonies, school of fish, etc., and any decentralized system in general. This course focusses on distributed computing in networks, in which the computing entities have to cope with uncertainty caused by spatial constraints. The design and analysis of distributed algorithms in networks is tightly connected to the following fields: graph theory, deterministic and randomized algorithms, communication complexity, interactive proofs, etc. The course may even provide examples of connections with algebraic topology, zero-knowledge proofs and quantum computing. Prequisite This course does not require specific prerequisite, other than basic knowledge in algorithm design and analysis, probability, and graph theory. Teaching material 2021-2022Lecture notes and/or slides sessions 1-2, 7-8, and 15 of the course Slides for sessions 3-6 of the course Slides for session 9 of the course Recommended books David Peleg. Distributed Computing: A Locality-Sensitive Approach. SIAM, Philadelphia, PA (2000) Leonid Barenboim, Michael Elkin. Distributed Graph Coloring: Fundamentals and Recent Developments. Synthesis Lectures on Distributed Computing Theory, Morgan and Claypool Publishers (2013) Exams
Rules for the partial and final exams:
Pedagogical team
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