Lecture

Replication and Consensus in Distributed Systems

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In course

CS-438: Decentralized systems engineering

A decentralized system is one that works when no single party is in charge or fully trusted. This course teaches decentralized systems principles while guiding students through the engineering of their own decentralized system featuring messaging, file sha ...

Description

This lecture covers the concepts of replication and consensus in distributed systems, focusing on storage nodes, RAID configurations, and the challenges of achieving agreement among multiple nodes. The instructor explains the fundamental challenges of trying again after failure and the importance of consistency in past attempts. The lecture also delves into the Paxos algorithm, majoritarian consensus, and the phases involved in reaching agreement. Additionally, it explores the concept of time-stop reservation and the need for a two-phase protocol to ensure successful agreement in distributed systems.

Instructors (2)

Pierluca Borsò

Expertise Teaching & PhD Courses Decentralized systems engineering CS-438 A decentralized system is one that works when no single party is in charge or fully trusted. This course teaches decentralized systems principles while guiding students through the engineering of their own decentralized system featuring messaging, file sharing, encryption, and blockchain concepts.

Bryan Alexander Ford

Links DEDIS lab home pagePersonal home page Curriculum vitae https://bford.info/cv.pdf Teaching & PhD PhD Students Shailesh Mishra Past EPFL PhD Students Eleftherios Kokoris Kogias, Kirill Nikitin, Cristina Basescu, Enis Ceyhun Alp, Simone Colombo, Pasindu Nivanthaka Tennage, Louis-Henri Manuel Jakob Merino Past EPFL PhD Students as codirector David Froelicher, Ludovic Barman, Georgia Fragkouli Courses Decentralized systems engineering CS-438 A decentralized system is one that works when n

Official source

Ontological neighbourhood

Computer engineering

Computer data storage: Topics in computer data storage

Related concepts (32)

Two-phase commit protocol

In transaction processing, databases, and computer networking, the two-phase commit protocol (2PC, tupac) is a type of atomic commitment protocol (ACP). It is a distributed algorithm that coordinates all the processes that participate in a distributed atomic transaction on whether to commit or abort (roll back) the transaction. This protocol (a specialised type of consensus protocol) achieves its goal even in many cases of temporary system failure (involving either process, network node, communication, etc.

Three-phase commit protocol

In computer networking and databases, the three-phase commit protocol (3PC) is a distributed algorithm which lets all nodes in a distributed system agree to commit a transaction. It is a more failure-resilient refinement of the two-phase commit protocol (2PC). A two-phase commit protocol cannot dependably recover from a failure of both the coordinator and a cohort member during the Commit phase. If only the coordinator had failed, and no cohort members had received a commit message, it could safely be inferred that no commit had happened.

Network-attached storage

Network-attached storage (NAS) is a file-level (as opposed to block-level storage) computer data storage server connected to a computer network providing data access to a heterogeneous group of clients. The term "NAS" can refer to both the technology and systems involved, or a specialized device built for such functionality (as unlike tangentially related technologies such as local area networks, a NAS device is often a singular unit). A NAS device is optimised for either by its hardware, software, or configuration.

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.

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