The GRID: Blueprint for a New Computing Infrastructure

By A. Marowka. Parallel Distributed Comput. Pract., 2000

5,477 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computational Theory and Mathematics, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/f414d9a3becd606acc18730a51542f3d55e0ad9f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computational Theory and Mathematics

In Hardware and Architecture

Citations in the corpus

Secure, Efficient Data Transport and Replica Management for High-Performance Data-Intensive Computing (2001, background): “The term Grid computing refers to the emerging computational and networking infrastructure that is designed to provide pervasive, uniform and reliable access to data, computational, and human resources distributed over wide area environments [6].”
The Semantic Grid: Past, Present, and Future (2005, background): “To characterize this movement, the term " Grid " was chosen to draw an analogy with the way in which the electricity power grid brought about a revolutionary change from the use of local electricity generators [1].”
Cloud monitoring: A survey (2013, background): “In fact the main objec-tive of a Grid is the sharing of resources across multiple organizations [132], implying simpler accounting criteria and limited resource abstraction, which creates a simple relation between monitoring parameters and physical resource status.”
SETI@home: an experiment in public-resource computing (2002, background): “The Global Grid Forum, formed in 1999, is developing projects collectively called The Grid for resource-sharing among academic and research organizations [2].”
New Grid Scheduling and Rescheduling Methods in the GrADS Project (2004, background): “The Grid [2] has become a promising development in high-performance computing fields over the past few years.”
The Eucalyptus Open-Source Cloud-Computing System (2009, methodology): “Finally, we provide evidence that EUCALYPTUS enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.”
Learning interpretable models (2006, methodology): “Furthermore, the world’s largest data warehouse in 2005 has been reported to have a size of 100 TB [Winter Corporation, 2005] and technologies like the Grid [Foster and Kesselman, 2004] propose a massive increase of data sizes in the near future.”
Pegasus: A framework for mapping complex scientific workflows onto distributed systems (2005, background): “There are however a few aspects of a distributed environment such as the Grid [15] and of the applications that make the problem more complex.”
Giggle: A Framework for Constructing Scalable Replica Location Services (2002, methodology): “This prototype relies on Grid Security Infrastructure and the globus_io socket layer from the Globus toolkit [17] to provide a server front end to a relational database.”
The AppLeS Parameter Sweep Template: User-Level Middleware for the Grid (2000, background): “I. Introduction Fast networks make it possible to aggregate CPU, network and storage resources into Computational Grids [5, 6].”
myGrid: personalised bioinformatics on the information grid (2003, background): “The Grid is proposed as the next generation infrastructure necessary to support and enable the collaboration of people and resources through highly capable computation and data management systems (Foster and Kesselman, 1998).”
Collaboration and Trust in Healthcare Innovation: The eDiaMoND Case Study (2005, background): “The concept of grid computing (Foster and Kesselman, 2004) is concerned with performing operations that span administrative domains.”
High-Performance Cloud Computing: A View of Scientific Applications (2009, methodology): “With the advent of Grid computing [2] new opportunities became available to scientists: in a complete analogy with the power Grid [3], the computing Grid could offer on demand the horse power required to perform large experiments, by relying on a network of machines, potentially extended all over th”