A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Using Kernel Couplings to Predict Parallel Application Performance
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Predicting application run times with historical information
Journal of Parallel and Distributed Computing
Distributed computing with Triana on the Grid: Research Articles
Concurrency and Computation: Practice & Experience
The Journal of Supercomputing
Predicting job start times on clusters
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Improving a Local Learning Technique for QueueWait Time Predictions
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Piloting an Empirical Study on Measures forWorkflow Similarity
SCC '06 Proceedings of the IEEE International Conference on Services Computing
Soft Benchmarks-Based Application Performance Prediction Using a Minimum Training Set
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
Overhead Analysis of Scientific Workflows in Grid Environments
IEEE Transactions on Parallel and Distributed Systems
Advanced data flow support for scientific grid workflow applications
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
A Probabilistic Model to Analyse Workflow Performance on Production Grids
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
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
Journal of Systems and Software
Computing resource prediction for mapreduce applications using decision tree
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
The Journal of Supercomputing
Future Generation Computer Systems
Toward fine-grained online task characteristics estimation in scientific workflows
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
A framework for dynamically generating predictive models of workflow execution
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
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Workflow execution time predictions for Grid infrastructures is of critical importance for optimized workflow executions, advance reservations of resources, and overhead analysis. Predicting workflow execution time is complex due to multeity of workflow structures, involvement of several Grid resources in workflow execution, complex dependencies of workflow activities and dynamic behavior of the Grid. In this paper we present an online workflow execution time prediction system exploiting similarity templates. The workflows are characterized considering the attributes describing their performance at different Grid infrastructural levels. A “supervised exhaustive search” is employed to find suitable templates. We also make a provision of including expert user knowledge about the workflow performance in the procession of our methods. Results for three real world applications are presented to show the effectiveness of our approach.