This international journal is directed to researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing.
The Journal of Parallel and Distributed Computing publishes original research papers and timely review articles on the theory, design, evaluation, and use of parallel and/or distributed computing systems. The journal also features special issues on these topics; again covering the full range from the design to the use of our targeted systems. Research Areas Include:
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This book rovides a comprehensive source of material on the principles and practice of distributed computer systems and the exciting new developments based on them, using a wealth of modern case studies to illustrate their design and development.
This book covers the principles, advanced concepts, and technologies of distributed systems in detail, including: communication, replication, fault tolerance, and security. It shows how distributed systems are designed and implemented in real systems.
This book tells the story of great potential, continued strength, and widespread international penetration of Grid computing. It highlights the international widespread coverage and unveils the future potential of the Grid.
To assist in understanding the more algorithmic parts, example programs in Python have been included. The examples in the book leave out many details for readability, but the complete code is available through the book's Website, hosted at www.distributed-systems.net. A personalized digital copy of the book is available for free, as well as a printed version through Amazon.com..
Abstract:This work studies a general distributed coded computing system based on the MapReduce-type framework, where distributed computing nodes within a half-duplex network wish to compute multiple output functions. We first introduce a definition of communication delay to characterize the time cost during the date shuffle phase, and then propose a novel coding strategy that enables parallel transmission among the computation nodes by delicately designing the data placement, message symbols encoding, data shuffling, and decoding. Compared to the coded distributed computing (CDC) scheme proposed by Li et al., the proposed scheme significantly reduces the communication delay, in particular when the computation load is relatively smaller than the number of computing nodes K. Moreover, the communication delay of CDC is a monotonically increasing function of K, while the communication delay of our scheme decreases as K increases, indicating that the proposed scheme can make better use of the computing resources.Keywords: map reduce; data shuffling; parallel computing; coded computing; distributed computing
The International Journal of Distributed Systems and Technologies (IJDST) focuses on integration techniques, methods, and tools employed in applied distributed computing systems, architectures, and technologies. IJDST pays particular attention to this dimension as a means of diversifying and broadening the applicability and scope of knowledge in the area of distributed systems and technologies. This journal is dedicated to push developmental boundaries and publish cutting-edge developments related to science-to-science, science-to-business, business-to-business, business-to-customer, and customer-to-customer interactions.Topics covered by IJDST include but are not limited to the heterogeneity and autonomy of Internet, Web, peer-to-peer, service oriented, grid, next generation grid, next generation technologies and other distributed systems paradigms and concepts; algorithms, software, application, services and technologies integration methods; process, resource, service and data virtualization, sharing, and integration; process, resource, service and resource flow management; scheduling; aspects of quality provision, policies; trust, identity management, forensics and security; application developments centered on the integration and management of various (e-) science-to-science, science-to-business, business-to-business, business-to-customer, and customer-to-customer models.
The mission of the International Journal of Distributed Systems and Technologies (IJDST) is to be a timely publication of original and scholarly research contributions, publishing papers in all aspects of the traditional and emerging areas of applied distributed systems and integration research (including data, agent, and mining technologies) such as next generation technologies, next generation grid, and distributed and throughput computing concepts, as well as related theories, applications, and integrations at various levels. Targeting researchers, practitioners, students, academicians, and industry professionals, IJDST provides an international forum for the dissemination of state-of-the-art theories, practices, and empirical research in distributed systems and prompts future community development as a means of promoting and sustaining a network of excellence.
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.[1][2] Distributed computing is a field of computer science that studies distributed systems.
Distributed computing also refers to the use of distributed systems to solve computational problems. In distributed computing, a problem is divided into many tasks, each of which is solved by one or more computers,[7] which communicate with each other via message passing.[8]
Distributed systems are groups of networked computers which share a common goal for their work.The terms "concurrent computing", "parallel computing", and "distributed computing" have much overlap, and no clear distinction exists between them.[18] The same system may be characterized both as "parallel" and "distributed"; the processors in a typical distributed system run concurrently in parallel.[19] Parallel computing may be seen as a particular tightly coupled form of distributed computing,[20] and distributed computing may be seen as a loosely coupled form of parallel computing.[10] Nevertheless, it is possible to roughly classify concurrent systems as "parallel" or "distributed" using the following criteria:
The study of distributed computing became its own branch of computer science in the late 1970s and early 1980s. The first conference in the field, Symposium on Principles of Distributed Computing (PODC), dates back to 1982, and its counterpart International Symposium on Distributed Computing (DISC) was first held in Ottawa in 1985 as the International Workshop on Distributed Algorithms on Graphs.[28]
Various hardware and software architectures are used for distributed computing. At a lower level, it is necessary to interconnect multiple CPUs with some sort of network, regardless of whether that network is printed onto a circuit board or made up of loosely coupled devices and cables. At a higher level, it is necessary to interconnect processes running on those CPUs with some sort of communication system.[29]
Another basic aspect of distributed computing architecture is the method of communicating and coordinating work among concurrent processes. Through various message passing protocols, processes may communicate directly with one another, typically in a master/slave relationship. Alternatively, a "database-centric" architecture can enable distributed computing to be done without any form of direct inter-process communication, by utilizing a shared database.[33] Database-centric architecture in particular provides relational processing analytics in a schematic architecture allowing for live environment relay. This enables distributed computing functions both within and beyond the parameters of a networked database.[34]
The field of concurrent and distributed computing studies similar questions in the case of either multiple computers, or a computer that executes a network of interacting processes: which computational problems can be solved in such a network and how efficiently? However, it is not at all obvious what is meant by "solving a problem" in the case of a concurrent or distributed system: for example, what is the task of the algorithm designer, and what is the concurrent or distributed equivalent of a sequential general-purpose computer?[citation needed]
In the analysis of distributed algorithms, more attention is usually paid on communication operations than computational steps. Perhaps the simplest model of distributed computing is a synchronous system where all nodes operate in a lockstep fashion. This model is commonly known as the LOCAL model. During each communication round, all nodes in parallel (1) receive the latest messages from their neighbours, (2) perform arbitrary local computation, and (3) send new messages to their neighbors. In such systems, a central complexity measure is the number of synchronous communication rounds required to complete the task.[48]
There are also fundamental challenges that are unique to distributed computing, for example those related to fault-tolerance. Examples of related problems include consensus problems,[51] Byzantine fault tolerance,[52] and self-stabilisation.[53]
Coordinator election algorithms are designed to be economical in terms of total bytes transmitted, and time. The algorithm suggested by Gallager, Humblet, and Spira [59] for general undirected graphs has had a strong impact on the design of distributed algorithms in general, and won the Dijkstra Prize for an influential paper in distributed computing. 2ff7e9595c
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