The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets

By Ann L. Chervenak, Ian T Foster, Carl Kesselman, C. Salisbury, Steven Tuecke. 2000

1,282 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture

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Citations in the corpus

Distributed learning of deep neural network over multiple agents (2018, background): “However, training of deep neural nets can be extremely data intensive requiring preparation of large scale datasets collected from multiple entities [1, 2].”
A community authorization service for group collaboration (2002, background): “We have found that communities often wish to grant permissions to collections of resources that belong to different organizations; for example, in data grids [ 33 ], multiple replicas of a file, which generally should have identical access control policies, typically exist on servers in different or”
Brain Meets Brawn: Why Grid and Agents Need Each Other (2004, background): “Early application drivers were largely from scientific computing [6, 10, 19], and included large-scale distributed computing [2, 15] (federation of computers), integration of large-scale data repositories (data grids [ 7 ]), collaboration [31], and tele-instrumentation [23, 26].”
A taxonomy and survey of grid resource management systems for distributed computing (2002, methodology): “For example, the Nimrod/G super-scheduler was used in conjunction with the existing Globus scheduling and resource components.”
The knowledge grid (2003, neutral): “To outline the type of services, we list the Globus generic services and Data Grid services [2] in Figure 1.”
Identifying Dynamic Replication Strategies for a High-Performance Data Grid (2001, background): “A data grid connects a collection of geographically distributed computer and storage resources [6] that may be located in different parts of a country or even in different countries, and enables users to share data and other resources.”
A taxonomy of Data Grids for distributed data sharing, management, and processing (2005, background): “At the minimum, a Data Grid provides two basic functionalities: a highperformance, reliable data transfer mechanism, and a scalable replica discovery and management mechanism [Chervenak et al. 2000].”
Secure, Efficient Data Transport and Replica Management for High-Performance Data-Intensive Computing (2001, background): “We have argued elsewhere [1] that the requirements of such distributed data intensive applications are best met by the creation of a Data Grid infrastructure that provides a set of orthogonal, application-independent services that can then be combined and specialized in different ways to meet the…”
Giggle: A Framework for Constructing Scalable Replica Location Services (2002, background): “Stockinger, H., et al. File and Object Replication in Data Grids. in 10th IEEE Intl. Symp. on High Performance Distributed Computing.”
Economic-based Distributed Resource Management and Scheduling for Grid Computing (2002, background): “Currently the Globus researchers are working together with the High-Energy Physics and the Climate Modeling community to build a data Grid [1].”
Data management and transfer in high-performance computational grid environments (2002, background): “We have argued elsewhere [1] that the requirements of such distributed data intensive applications are best met by the creation of a Data Grid infrastructure that provides a set of orthogonal, application-independent services that can then be combined and specialized in different ways to meet the…”
Chimera: a virtual data system for representing, querying, and automating data derivation (2002, methodology): “However, these relations are frequently implemented via a separate metadata catalog [7] and/or replica catalog [11] and so they are not considered here.”
Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012, neutral): “resources [24].”
The GrADS Project: Software Support for High-Level Grid Application Development (2001, background): “In addition, the NSF GriPhyN project is developing a distributed analysis environment for physics experiments that will serve thousands of users (Chervenak et al., 2000).”