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Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers. The components of a distributed system communicate and coordinate their actions by passing messages to
Every request received by a non-failing node in the system must result in a response. This is the definition of availability in CAP theorem as defined by Gilbert and Lynch. Note that availability as defined in CAP theorem is different from high availability in software architecture. Partition tolerance
The specification discussed the architecture of multi-computer systems, preferring peer-to-peer rather than master-slave. Each member of such an interconnected group of separate computers is free at any time to initiate and dispatch special control orders to any of its partners in the system. As a consequence, the supervisory control over the common task may initially be loosely distributed ...
The Distributed Computing Environment ( DCE) is a software system developed in the early 1990s from the work of the Open Software Foundation (OSF), a consortium founded in 1988 that included Apollo Computer (part of Hewlett-Packard from 1989), IBM, Digital Equipment Corporation, and others. [1] [2] The DCE supplies a framework and a toolkit for ...
Paxos (computer science) Paxos is a family of protocols for solving consensus in a network of unreliable or fallible processors. Consensus is the process of agreeing on one result among a group of participants. This problem becomes difficult when the participants or their communications may experience failures.
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.
The originally listed fallacies are [1] The network is reliable; Latency is zero; Bandwidth is infinite; The network is secure; Topology doesn't change; There is one administrator; Transport cost is zero; The network is homogeneous;
Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems. It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets.