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
Discovering Piecewise Linear Models of Grid Workload
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
An evaluation of the benefits of fine-grained value-based scheduling on general purpose clusters
Future Generation Computer Systems
An evaluation of the benefits of fine-grained value-based scheduling on general purpose clusters
Future Generation Computer Systems
Negotiation-Based Scheduling of Scientific Grid Workflows Through Advance Reservations
Journal of Grid Computing
Journal of Systems and Software
Journal of Grid Computing
Towards Non-Stationary Grid Models
Journal of Grid Computing
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
Impact of variable priced cloud resources on scientific workflow scheduling
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Future Generation Computer Systems
Online cost-efficient scheduling of deadline-constrained workloads on hybrid clouds
Future Generation Computer Systems
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Application execution time prediction is of key importance in making decisions about efficient usage of Grid resources. Grid services lack support of a generic application execution time prediction service due to environment specific solutions provided by the existing prediction techniques. To remedy this, we present a generic and comprehensive system to provide execution time predictions of applications on different Grid-sites. Our system is based on a two layered training phase to minimize the training effort, which is our first main contribution. The training phase is driven by a novel experimental design. We also introduce a mechanism of sharing performance measurements across the Grid, on the basis of soft benchmarks, which is our second contribution. Both of these phases support our prediction engine to serve robust predictions. Experiments from the prototype implementation are shown to demonstrate the effectiveness of our proposed system.