The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Data preparation for data mining
Data preparation for data mining
Host load prediction using linear models
Cluster Computing
Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications
Proceedings of the 2001 International Conference on Parallel Architectures and Compilation Techniques
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
Improving resource selection and scheduling using predictions
Grid resource management
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
QBETS: queue bounds estimation from time series
Proceedings of the 2007 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Backfilling Using System-Generated Predictions Rather than User Runtime Estimates
IEEE Transactions on Parallel and Distributed Systems
An Architecture for an Adaptive Run-time Prediction System
ISPDC '08 Proceedings of the 2008 International Symposium on Parallel and Distributed Computing
ISPDC '08 Proceedings of the 2008 International Symposium on Parallel and Distributed Computing
Trace-based evaluation of job runtime and queue wait time predictions in grids
Proceedings of the 18th ACM international symposium on High performance distributed computing
Journal of Systems and Software
A grid workflow Quality-of-Service estimation based on resource availability prediction
The Journal of Supercomputing
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In heterogeneous and distributed environments it is necessary to create schedules for utilising resources in an efficient way. This generation often poses a problem for a scheduler, since several aspects have to be considered. One way of supporting a scheduler is to provide accurate predictions of the run-times of the submitted jobs. A large number of current techniques offer statistical models that are deployed on previously filtered data. As users have different jobs, and because the attributes of their jobs differ, filtering data and choosing an appropriate prediction method has to cover these aspects. This article describes Adaps, a system for run-time prediction that works in three phases. Each is independently adjusting to the jobs of a user, based on historical information. This leads to a user specific clustering of data and to a flexible utilisation of different prediction techniques in order to create a user-centred prediction model.