Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
GUESS: a language and interface for graph exploration
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Job Failure Analysis and Its Implications in a Large-Scale Production Grid
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
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Large-scale statistical analysis of more than 28 million jobs collected during 20 months of grid activity was undertaken in order to examine the relations between users, computing elements and jobs in the network. The results give insight into the global system behaviour and can be used to build models applicable in various contexts of grid computing. As an example, we here construct probabilistic models that prove to be able to accurately predict job abortion.