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In mathematics and computer science, an algorithm (ˈælɡərɪðəm) is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing calculations and data processing. More advanced algorithms can use conditionals to divert the code execution through various routes (referred to as automated decision-making) and deduce valid inferences (referred to as automated reasoning), achieving automation eventually.
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.
In a distributed computing system, a failure detector is a computer application or a subsystem that is responsible for the detection of node failures or crashes. Failure detectors were first introduced in 1996 by Chandra and Toueg in their book Unreliable Failure Detectors for Reliable Distributed Systems. The book depicts the failure detector as a tool to improve consensus (the achievement of reliability) and atomic broadcast (the same sequence of messages) in the distributed system.
Transmission faults allow us to reason about permanent and transient value faults in a uniform way. However, all existing solutions to consensus in this model are either in the synchronous system, or
Consensus is one of the key problems in fault-tolerant distributed computing. Although the solvability of consensus is now a well-understood problem, comparing different algorithms in terms of efficie