6 edition of Distributed data management for grid computing found in the catalog.
Includes bibliographical references (p. 273-275)
|Statement||Michael Di Stefano.|
|LC Classifications||QA76.9.C58 D57 2005, QA76.9.C58 D57 2005|
|The Physical Object|
|Pagination||xxi, 285 p. :|
|Number of Pages||285|
|LC Control Number||2004031017|
Grid Computing requires the use of software that can divide and farm out pieces of a program to as many as several thousand computers. This book explores processes and techniques needed to create a successful Grid . Abstract: This paper presents a model for smart grid data management based on specific characteristics of cloud computing, such as distributed data management for real-time data gathering, parallel processing for real-time information retrieval, and ubiquitous access. The appliance of the cloud computing model meets the requirements of data and computing intensive smart grid .
A data grid is an architecture or set of services that gives individuals or groups of users the ability to access, modify and transfer extremely large amounts of geographically distributed data for research purposes. Data grids make this possible through a host of middleware applications and services that pull together data . Grid computing has been around for over 12 years now and its advantages are many. Grid computing can be defined in many ways but for these discussions let's simply call it a way to execute compute jobs (e.g. perl scripts, database queries, etc.) across a distributed set of resources instead of one central resource. In the past most computing .
are both compute and data intensive, therefore the Grid can oﬀers a computing and data management infrastructure for supporting decentralized and parallel data analysis. This paper discusses how Grid computing can be used to support distributed data mining. Grid-based data . The 9 revised full papers presented were carefully reviewed and selected from 15 submissions. The papers are organized in topical sections on data management in the cloud, cloud MapReduce and performance evaluation, and data stream systems and distributed data .
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This evolution in distributed computing is leading a paradigm shift in leveraging widely distributed architectures to get the most processing power per IT dollar. Presenting a solid foundation of data management issues and techniques, this practical book delves into grid architecture.
This evolution in distributed computing is leading a paradigm shift in leveraging widely distributed architectures to get the most processing power per IT dollar.
Presenting a solid foundation of data management issues and techniques, this practical book delves into grid architecture Cited by: This evolution in distributed computing is leading a paradigm shift in leveraging widely distributed architectures to get the most processing power per IT dollar.
Presenting a solid foundation of data management issues and techniques, this practical book delves into grid architecture.
This evolution in distributed computing is leading a paradigm shift in leveraging widely distributed architectures to get the most processing power per IT ting a solid foundation of data management issues and techniques, this practical book delves into grid architecture, services, practices, and much more, including:* Why businesses should adopt grid computing* How to master.
Distributed data management for grid computing. [Michael Di Stefano] -- "The author has tailored this publication for two distinct audiences. Business professionals will gain a better understanding of how grid computing improves productivity. Presenting a robust foundation of data administration factors and strategies, this smart book delves into grid construction, suppliers, practices, and slightly extra, along with: * Why corporations should undertake grid computing * How one can grasp the basic concepts.
Grid Computing represents a fundamental paradigm shift away from client/server as client server was to the mainframe/mini. As a result data management in this highly distributed compute. Distributed computing, particularly Grid computing environment involves transporting huge volume of data across geographically spread sources and destinations managed by an efficient data.
Distributed Data Management for Grid Computing Grid Computing is entering the IT mainstream driven by cost controls, business needs, and technology conditions. This book is developed by practitioners. Distributed data management for grid computing.
[Michael Di Stefano] -- Discover grid computing-how to successfully build, implement, and manage widely distributed computing architectureWith technology. PART II DATA T IN GRID COMPUTING 5 Scaling in the Grit. Topology Evolution in Data Management. 43 Client/Server Evolution, 44 Grid Evolution, 44 Dif'l'erent Implementalions of a Data Grid, 45 Level 0 Data Grids, 45 FTP in Grid, 46 Distributed Filing Systems, 47 Faster Servers, 47 Metadata Hubs and Distributed Data.
Big data storage management is one of the most challenging issues for Grid computing environments, since large amount of data intensive applications frequently involve a high degree of data access. Uncover grid computing-find out how to efficiently construct, implement, and handle extensively distributed computing structure With technology budgets beneath growing scrutiny and system.
It presents an overview of grid and cloud database management, with topics ranging from standards and specifications to research projects and case studies. I recommend this book not only as a technical reference for researchers, students, programmers, and systems engineers, but also as an introduction to cloud and grid computing Format: Hardcover.
1 DISTRIBUTED AND BIG DATA STORAGE MANAGEMENT IN GRID COMPUTING Ajay Kumar1 and Seema Bawa2 1Department of Computer Science and IT, Mewar University, Chittorgarh, INDIA [email protected] 2Department of Computer Engineering, Thapar University, Patiala, INDIA [email protected] Abstract Big data storage management is one of the most challenging issues for Grid computing.
Parallel, Distributed, and Grid-Based Data Mining: Algorithms, Systems, and Applications: /ch Knowledge discovery has become a necessary task in scientific, life sciences, and business fields, both for the growing amount of data Cited by: 6.
Micro-Services: A Service-Oriented Paradigm for Scalable, Distributed Data Management: /ch Service-oriented architectures (SOA) enable orchestration of Cited by: 2. The China Clipper Project: A Data Intensive Grid Support for Dynamically Configured, Adaptive, Distributed, High-Performance Data and Computing Environments.
Proc. of Computing Cited by: This volume presents the latest Grid solutions and research results in key areas such as distributed storage management, Grid databases, Semantic Grid and Grid-aware data mining.
Written for a. Discover grid computing-how to successfully build, implement, and manage widely distributed computing architecture With technology budgets under increasing scrutiny and system architecture.
Described as a distributed resource management tool, Grid Engine allows engineers at companies like Sony and Synopsys to pool the computer cycles on up to 80 workstations at a time. "Grid Computing: Features contributions from the major players in the field; Covers all aspects of grid ."Grid is often used in a generalised context to refer to distributed computing.
But essentially, I see it as exploiting the resources of the network to solve problems, with access to more Author: Techtarget.Grid computing is the use of widely distributed computer resources to reach a common goal.
A computing grid can be thought of as a distributed system with non-interactive workloads that involve many files. Grid computing is distinguished from conventional high-performance computing systems such as cluster computing in that grid .