Efficient Response Time Predictions by Exploiting Application and Resource State Similarities
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Backfilling Using System-Generated Predictions Rather than User Runtime Estimates
IEEE Transactions on Parallel and Distributed Systems
Mining performance data for metascheduling decision support in the grid
Future Generation Computer Systems - Special section: Data mining in grid computing environments
Removing the need for state dissemination in grid resource brokering
Proceedings of the 5th international workshop on Middleware for grid computing: held at the ACM/IFIP/USENIX 8th International Middleware Conference
Future Generation Computer Systems
Enhancing Prediction on Non-dedicated Clusters
Euro-Par '08 Proceedings of the 14th international Euro-Par conference on Parallel Processing
Using historical accounting information to predict the resource usage of grid jobs
Future Generation Computer Systems
Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Performance problems of using system-predicted runtimes for parallel job scheduling
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
Using on-the-fly simulation for estimating the turnaround time on non-dedicated clusters
Euro-Par'06 Proceedings of the 12th international conference on Parallel Processing
Modeling user runtime estimates
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
Scheduling in HC and Grids Using a Parallel CHC
Computational Intelligence
Future Generation Computer Systems
State-based predictions with self-correction on Enterprise Desktop Grid environments
Journal of Parallel and Distributed Computing
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In a computational Grid which consists of many computer clusters, job start time predictions are useful to guide resource selections and balance the workload distribution. However, the basic Grid middleware available today either has no means of expressing the time that a site will take before starting a job or uses a simple linear scale. In this paper we introduce a system for predicting job start times on clusters. Our predictions are based on statistical analysis of historical job traces and simulation of site schedulers. We have deployed the system on the EDG (European Data-Grid) production cluster at NIKHEF. The experimental results show that acceptable prediction accuracy is achieved to reflect real site states and site-specific scheduling policies. We find that the average error of our job start time predictions is 18.9 percent of the average job queue wait time and this is around 20 times smaller than the average prediction error using the EDG solution.