Prediction-based auto-scaling of scientific workflows
Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science
Job and data clustering for aggregate use of multiple production cyberinfrastructures
Proceedings of the fifth international workshop on Data-Intensive Distributed Computing Date
Enabling large-scale scientific workflows on petascale resources using MPI master/worker
Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond
A survey of task mapping on production grids
ACM Computing Surveys (CSUR)
Preference---Based Matchmaking of Grid Resources with CP---Nets
Journal of Grid Computing
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Grid computing has become very popular in big and widespread scientific communities with high computing demands, like high energy physics. Computing resources are being distributed over many independent sites with only a thin layer of Grid middleware shared between them. This deployment model has proven to be very convenient for computing resource providers, but has introduced several problems for the users of the system, the three major being the complexity of job scheduling, the non-uniformity of compute resources, and the lack of good job monitoring.Pilot jobs address all the above problems by creating a virtual private computing pool on top of Grid resources. This paper presents both the general pilot concept, as well as a concrete implementation, called glideinWMS, deployed in the Open Science Grid.