Abstract. The objective of the European DataGrid (EDG) project is to assist the next generation of scientific exploration, which requires intensive computation and analysis of shared large-scale datasets, from hundreds of terabytes to petabytes, across widely distributed scientific communities. We see these requirements emerging in many scientific disciplines, including physics, biology, and earth sciences. Such sharing is made complicated by the distributed nature of the resources to be used, the distributed nature of the research communities, the size of the datasets and the limited network bandwidth available. To address these problems we are building on emerging computational Grid technologies to establish a research network that is developing the technology components essential for the implementation of a world-wide data and computational Grid on a scale not previously attempted. An essential part of this project is the phased development and deployment of a large-scale Grid testbed.The primary goals of the first phase of the EDG testbed were: 1) to demonstrate that the EDG software components could be integrated into a productionquality computational Grid; 2) to allow the middleware developers to evaluate the design and performance of their software; 3) to expose the technology to end-users to give them hands-on experience; and 4) to facilitate interaction and feedback between end-users and developers. This first testbed deployment was achieved towards the end of 2001 and assessed during the successful European Union review of the project on March 1, 2002. In this article we give an overview of the current status and plans of the EDG project and describe the distributed testbed.
European DataGrid Project
An overview is presented of the characteristics of HEP computing and its mapping to the Grid paradigm. This is followed by a synopsis of the main experiences and lessons learned by HEP experiments in their use of DataGrid middleware using both the EDG application testbed and the LCG production service. Particular reference is made to experiment ‘data challenges’, and a forward look is given to necessary developments in the framework of the EGEE project
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