Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
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
Two experiments with application-level quality of service on the EGEE grid
Proceedings of the 2nd workshop on Grids meets autonomic computing
Discovering Piecewise Linear Models of Grid Workload
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Processing moldable tasks on the grid: Late job binding with lightweight user-level overlay
Future Generation Computer Systems
Self-adaptive deployment of parametric sweep applications through a complex networks perspective
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Towards Non-Stationary Grid Models
Journal of Grid Computing
Euro-Par'12 Proceedings of the 18th international conference on Parallel processing workshops
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On the grid log data of more than 28 million jobs collected during 20 months, we use statistical and data mining methods to examine the relations primarily between users, computing elements and jobs in the network. The results of the large-scale analysis are used for building probabilistic models of the system behaviour. Bayesian Networks are constructed on historical data and are proven to be able to accurately predict abortion and lengths of newly arriving jobs.