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Distributed data processing. Distributed data processing [1] (DDP) [2] was the term that IBM used for the IBM 3790 (1975) and its successor, the IBM 8100 (1979). Datamation described the 3790 in March 1979 as "less than successful." [3] [4] Distributed data processing was used by IBM to refer to two environments: IMS DB/DC; CICS/DL/I [5] [6]
Some researchers have made a functional and experimental analysis of several distributed file systems including HDFS, Ceph, Gluster, Lustre and old (1.6.x) version of MooseFS, although this document is from 2013 and a lot of information are outdated (e.g. MooseFS had no HA for Metadata Server at that time).
Distributed Data Management Architecture (DDM) is IBM's open, published software architecture for creating, managing and accessing data on a remote computer. DDM was initially designed to support record-oriented files; it was extended to support hierarchical directories, stream-oriented files, queues, and system command processing; it was further extended to be the base of IBM's Distributed ...
Stream processing is especially suitable for applications that exhibit three application characteristics: [citation needed] Compute intensity, the number of arithmetic operations per I/O or global memory reference. In many signal processing applications today it is well over 50:1 and increasing with algorithmic complexity.
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. [1] [2] The components of a distributed system communicate and coordinate their actions by passing messages to
The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires:
In distributed computing, distributed objects [citation needed] are objects (in the sense of object-oriented programming) that are distributed across different address spaces, either in different processes on the same computer, or even in multiple computers connected via a network, but which work together by sharing data and invoking methods.
The International Parallel and Distributed Processing Symposium (or IPDPS) is an annual conference for engineers and scientists to present recent findings in the fields of parallel processing and distributed computing. In addition to technical sessions of submitted paper presentations, the meeting offers workshops, tutorials, and commercial ...