Fundamentals of neural networks: architectures, algorithms, and applications
Fundamentals of neural networks: architectures, algorithms, and applications
Future Generation Computer Systems - Special issue on metacomputing
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Predicting Queue Times on Space-Sharing Parallel Computers
Predicting Queue Times on Space-Sharing Parallel Computers
ASKALON: a tool set for cluster and Grid computing: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
SKG '06 Proceedings of the Second International Conference on Semantics, Knowledge, and Grid
Grid'5000: A Large Scale and Highly Reconfigurable Grid Experimental Testbed
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Efficient Response Time Predictions by Exploiting Application and Resource State Similarities
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Feature selection using principal feature analysis
Proceedings of the 15th international conference on Multimedia
Predicting the execution time of grid workflow applications through local learning
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
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
Execution time prediction for parallel data processing tasks
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Performance improvement in collaborative recommendation using multi-layer perceptron
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Predicting the Execution Time of Workflow Activities Based on Their Input Features
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
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An accurate performance prediction service can be very useful for resource management and the scheduler service and help them make better resource utilization decisions by providing better execution time estimates. In this paper we present a novel approach of predicting the execution time of computational tasks for Grid infrastructures using machine learning models based on multilayer perceptron combined with a principal feature selection algorithm for selecting the most important runtime features. Our technique uses runtime provenance information as input to multilayer perceptron (MLP) based neural network which trains a model that can predict the execution time of programs with reasonably good accuracy. For the development and training of our machine learning models, we used provenance data collected from three different computational Grids by executing real-world applications. By using our MLP based method we were able to minimise the prediction error to as low as 22% for real-world applications on various Grid infrastructures.