Lecture

Introduction to Reliable Distributed Programming

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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 introduces the fundamentals of distributed algorithms, covering topics such as the history of algorithms, the definition of algorithms, distributed systems, reliable broadcast, shared memory, consensus, and more. The instructor emphasizes the importance of understanding distributed algorithms for building reliable and secure distributed systems.

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 (32)

Time complexity

In computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm, supposing that each elementary operation takes a fixed amount of time to perform. Thus, the amount of time taken and the number of elementary operations performed by the algorithm are taken to be related by a constant factor.

Computational complexity

In computer science, the computational complexity or simply complexity of an algorithm is the amount of resources required to run it. Particular focus is given to computation time (generally measured by the number of needed elementary operations) and memory storage requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem. The study of the complexity of explicitly given algorithms is called analysis of algorithms, while the study of the complexity of problems is called computational complexity theory.

Computational complexity theory

In theoretical computer science and mathematics, computational complexity theory focuses on classifying computational problems according to their resource usage, and relating these classes to each other. A computational problem is a task solved by a computer. A computation problem is solvable by mechanical application of mathematical steps, such as an algorithm. A problem is regarded as inherently difficult if its solution requires significant resources, whatever the algorithm used.

Randomized algorithm

A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic or procedure. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random determined by the random bits; thus either the running time, or the output (or both) are random variables.

Space complexity

The space complexity of an algorithm or a computer program is the amount of memory space required to solve an instance of the computational problem as a function of characteristics of the input. It is the memory required by an algorithm until it executes completely. This includes the memory space used by its inputs, called input space, and any other (auxiliary) memory it uses during execution, which is called auxiliary space. Similar to time complexity, space complexity is often expressed asymptotically in big O notation, such as etc.

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