Communications of the ACM
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Predicting Queue Times on Space-Sharing Parallel Computers
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Resource management in metacomputing environments (parallel computing)
Resource management in metacomputing environments (parallel computing)
PGGA: a predictable and grouped genetic algorithm for job scheduling
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
Predict task running time in grid environments based on CPU load predictions
Future Generation Computer Systems
Deadline missing predictor based on aperiodic server queue length for distributed systems
Computer Communications
Predicting Running Time of Grid Tasks based on CPU Load Predictions
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
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
A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Predicting the execution time of grid workflow applications through local learning
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
PGGA: A predictable and grouped genetic algorithm for job scheduling
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
Auction resource allocation mechanisms in grids of heterogeneous computers
WSEAS Transactions on Computers
On the Use of Machine Learning to Predict the Time and Resources Consumed by Applications
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Task profiling model for load profile prediction
Future Generation Computer Systems
Journal of Systems and Software
On/off-line prediction applied to job scheduling on non-dedicated NOWs
Journal of Computer Science and Technology - Special issue on natural language processing
Resource optimization in distributed real-time multimedia applications
Multimedia Tools and Applications
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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We present a technique for predicting the run times of parallel applications based upon the run times of ''similar'' applications that have executed in the past. The novel aspect of our work is the use of search techniques to determine those application characteristics that yield the best definition of similarity for the purpose of making predictions. We use four workloads recorded from parallel computers at Argonne National Laboratory, the Cornell Theory Center, and the San Diego Supercomputer Center to evaluate the effectiveness of our approach. We show that on these workloads our techniques achieve predictions that are between 21 and 64 percent better than those achieved by other techniques; our approach achieves mean prediction errors that are between 29 and 59 percent of mean application run times.