Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Consensus: Distributed Computing Algorithms

In course

CS-451: Distributed algorithms

Computing is nowadays distributed over several machines, in a local IP-like network, a cloud or a P2P network. Failures are common and computations need to proceed despite partial failures of machines or communication links. This course will study the fo ...

Description

This lecture covers the consensus problem in distributed computing, where processes propose values and aim to agree on one value. Key concepts include uniform consensus properties, consensus algorithms, correctness arguments, and the importance of maintaining consistency in distributed systems.

This video is available exclusively on Mediaspace for a restricted audience. Please log in to MediaSpace to access it if you have the necessary permissions.

Watch on Mediaspace

Instructor

Rachid Guerraoui

Rachid Guerraoui has been affiliated with Ecole des Mines of Paris, the Commissariat à l'Energie Atomique of Saclay, Hewlett Packard Laboratories and the Massachusetts Institute of Technology. He has worked in a variety of aspects of distributed computing, including distributed algorithms and distributed programming languages. He is most well known for his work on (e-)Transactions, epidemic information dissemination and indulgent algorithms. He co-authored a book on Transactional Systems (Hermes) and a book on reliable distributed programming (Springer). He was appointed program chair of ECOOP 1999, ACM Middleware 2001, IEEE SRDS 2002, DISC 2004 and ACM PODC 2010. His publications are available at http://lpdwww.epfl.ch/rachid/papers/generalPublis.html

Official source

Related concepts (28)

Consensus (computer science)

A fundamental problem in distributed computing and multi-agent systems is to achieve overall system reliability in the presence of a number of faulty processes. This often requires coordinating processes to reach consensus, or agree on some data value that is needed during computation. Example applications of consensus include agreeing on what transactions to commit to a database in which order, state machine replication, and atomic broadcasts.

Distributed algorithm

A distributed algorithm is an algorithm designed to run on computer hardware constructed from interconnected processors. Distributed algorithms are used in different application areas of distributed computing, such as telecommunications, scientific computing, distributed information processing, and real-time process control. Standard problems solved by distributed algorithms include leader election, consensus, distributed search, spanning tree generation, mutual exclusion, and resource allocation.

Distributed computing

A distributed system is a system whose components are located on different networked computers, which communicate and coordinate their actions by passing messages to one another. Distributed computing is a field of computer science that studies distributed systems. The components of a distributed system interact with one another in order to achieve a common goal. Three significant challenges of distributed systems are: maintaining concurrency of components, overcoming the lack of a global clock, and managing the independent failure of components.

Distributed operating system

A distributed operating system is system software over a collection of independent software, networked, communicating, and physically separate computational nodes. They handle jobs which are serviced by multiple CPUs. Each individual node holds a specific software subset of the global aggregate operating system. Each subset is a composite of two distinct service provisioners. The first is a ubiquitous minimal kernel, or microkernel, that directly controls that node's hardware.

P (complexity)

In computational complexity theory, P, also known as PTIME or DTIME(nO(1)), is a fundamental complexity class. It contains all decision problems that can be solved by a deterministic Turing machine using a polynomial amount of computation time, or polynomial time. Cobham's thesis holds that P is the class of computational problems that are "efficiently solvable" or "tractable". This is inexact: in practice, some problems not known to be in P have practical solutions, and some that are in P do not, but this is a useful rule of thumb.

Related lectures (18)

Replication and Consensus in Distributed Systems

Explores replication, consensus, Paxos algorithm, majoritarian consensus, and time-stop reservation in distributed systems.

Decentralized Systems Engineering

Explores decentralized systems engineering, covering database initialization, reliable messaging, consensus algorithms, and encryption protocols.

Resource-aware security and privacy in IoT domain MOOC: IoT Systems and Industrial Applications with Design Thinking

Explores challenges in the IoT era, resource-constrained nodes, complex algorithms, security solutions, and blockchain adoption for secure data storage.

Limitations of Bitcoin and Increasing TPS

Discusses the limitations of Bitcoin and ways to increase transaction processing speed.

Blockchain Technology: Decentralized Systems and IoT Applications MOOC: IoT Systems and Industrial Applications with Design Thinking