Heuristic-based scheduling to maximize throughput of data-intensive grid applications
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
Computers and Operations Research
Hopfield neural network for simultaneous job scheduling and data replication in grids
Future Generation Computer Systems
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Future high energy physics experiments will require huge distributed computational infrastructures, called data grids, to satisfy their data processing and analysis needs. This paper records the current understanding of the demands that will be put on a data grid around 2006, by the hundreds of physicists working with data from the CMS experiment. The current understanding is recorded by defining a model of this CMS physics analysis application running on a 'virtual data grid' as proposed by the GriPhyN project. The complete model consists of a hardware model, a data model, and an application workload model. The main utility of the HEPGRID2001 model is that it encodes high energy physics (HEP) application domain knowledge and makes it available in a form that is understandable for the CS community, so that architectural and performance requirements for data grid middleware components can be derived.