A static performance estimator to guide data partitioning decisions
PPOPP '91 Proceedings of the third ACM SIGPLAN symposium on Principles and practice of parallel programming
Analytical performance prediction on multicomputers
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Static dependent costs for estimating execution time
LFP '94 Proceedings of the 1994 ACM conference on LISP and functional programming
Machine learning of rules and trees
Machine learning, neural and statistical classification
Machine Learning - Special issue on learning with probabilistic representations
Performance Prediction Technology for Agent-Based Resource Management in Grid Environments
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Run-Time Statistical Estimation of Task Execution Times for Heterogeneous Distributed Computing
HPDC '96 Proceedings of the 5th IEEE International Symposium on High Performance Distributed Computing
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
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
The Journal of Supercomputing
A performance prediction framework for scientific applications
Future Generation Computer Systems
Contention-sensitive static performance prediction for parallel distributed applications
Performance Evaluation
Full Bayesian network classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Methods of inference and learning for performance modeling of parallel applications
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Efficient Response Time Predictions by Exploiting Application and Resource State Similarities
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Overhead Analysis of Scientific Workflows in Grid Environments
IEEE Transactions on Parallel and Distributed Systems
Performance prediction with skeletons
Cluster Computing
A regression-based approach to scalability prediction
Proceedings of the 22nd annual international conference on Supercomputing
Performance prediction of large-scale parallell system and application using macro-level simulation
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
An approach to performance prediction for parallel applications
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Grid resource selection by application benchmarking for computational haemodynamics applications
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
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
Characterizing quality of resilience in scientific workflows
Proceedings of the 6th workshop on Workflows in support of large-scale science
Computing resource prediction for mapreduce applications using decision tree
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Automating Data-Throttling Analysis for Data-Intensive Workflows
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
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
Future Generation Computer Systems
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Grid schedulers require individual activity performance predictions to map workflow activities on different Grid sites. The effectiveness of the scheduling systems is hampered by inaccurate predictions due to the inability of existing predictors to effectively model the dynamic and heterogeneous nature of Grid resources, or the wide range of problem sizes and runtime arguments. To address this deficiency, we propose a hybrid Bayesian-neural network approach to dynamically model and predict the execution time of activities in real workflow applications. Bayesian network is used for a high-level representation of activities performance probability distribution against different factors affecting the performance. The important attributes are dynamically selected by the Bayesian network and fed into a radial basis function neural network to make further predictions. Our approach is generic to any type of scientific applications, and flexible to import expert knowledge to further improve accuracies. Experimental results for activities from three realworld workflow applications are presented to show effectivenessof our approach.