IEEE Transactions on Parallel and Distributed Systems
Machine Learning
A Historical Application Profiler for Use by Parallel Schedulers
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
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
Improving MapReduce performance in heterogeneous environments
OSDI'08 Proceedings of the 8th USENIX conference on Operating systems design and implementation
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The cloud computing paradigm offer users access to computing resource in a pay-as-you-go manner. However, to both cloud computing vendors and users, it is a challenge to predict how much resource is needed to run an application in a cloud at a required level of quality. This research focuses on developing a model to predict the computing resource consumption of MapReduce applications in the cloud computing environment. Based on the Classified and Regression Tree (CART), the proposed approach derives knowledge of the relationship among the application features, quality of service, and amount of computing resource, from a small training. The experiments show that the prediction accuracy is as high as 80%. This research can potentially benefit both the cloud vendors and users through improving resource management and reducing costs.