Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Evaluation of parallel execution of program tree structures
SIGMETRICS '84 Proceedings of the 1984 ACM SIGMETRICS conference on Measurement and modeling of computer systems
Using Kernel Couplings to Predict Parallel Application Performance
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
A review of machine learning in dynamic scheduling of flexible manufacturing systems
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Kepler: An Extensible System for Design and Execution of Scientific Workflows
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Predicting application run times with historical information
Journal of Parallel and Distributed Computing
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
Network Bandwidth Predictor (NBP): A System for Online Network performance Forecasting
CCGRID '06 Proceedings of the Sixth 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
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
Fast compiler optimisation evaluation using code-feature based performance prediction
Proceedings of the 4th international conference on Computing frontiers
ASKALON: A Grid Application Development and Computing Environment
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
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
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
Future Generation Computer Systems
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Planning for execution of scientific workflow applications in the Grid requires in advance prediction of workflow execution time for optimized execution of these applications. However, predicting execution times of such applications is very complex mainly due to different structures of workflows, possible parallel execution of workflow tasks on multiple resources and the dynamic and heterogeneous nature of the Grid. In this paper, we describe an optimized method (in extension to a previous work by Nadeem et al. (2009) [4]) for execution time prediction of workflow applications in the Grid. We characterize workflows in terms of attributes describing their structures and performance during different stages of their execution. Overall, performance of the workflows is modeled through templates of workflow attributes. An optimized method exploiting evolutionary programming is employed to search for suitable templates. Three different induction models are employed to generate predictions and later compared for their accuracy. The results from our experiments for three real-world workflow applications on a real Grid are presented to show the effectiveness of our approach. We also compare the proposed approach with our previous method based on supervised exhaustive search by Nadeem and Fahringer (2009) [4].