Aprendizaje de Modelos de Predicción del Tiempo de Ejecución para Flujos de Trabajo de Análisis de Expresiones de Genes
(Learning Running-time Prediction Models for Gene-Expression Analysis Workflows)
David A. Monge (email@example.com)2, Matej Holec1, Filip Zelezny1, Carlos García Garino2
1IDA, Czech Technical University2ITIC, Universidad Nacional de Cuyo
This paper appears in: Revista IEEE América Latina
Publication Date: Sept. 2015
Volume: 13, Issue: 9
One of the central issues for the efficient management of Scientific workflow applications is the prediction of tasks performance. This paper proposes a novel approach for constructing performance models for tasks in data-intensive scientific workflows in an autonomous way. Ensemble Machine Learning techniques are used to produce robust combined models with high predictive accuracy. Information derived from workflow systems and the characteristics and provenance of the data are exploited to guarantee the accuracy of the models. A gene-expression analysis workflow application was used as case study over homogeneous and heterogeneous computing environments. Experimental results evidence noticeable improvements while using ensemble models in comparison with single/standalone prediction models. Ensemble learning techniques made it possible to reduce the prediction error with respect to the strategies of a single-model with values ranging from 14.47 percent to 28.36 percent for the homogeneous case, and from 8.34 percent to 17.18 percent for the heterogeneous case.
Performance Prediction, Ensemble Learning, Workflows, Bioinformatics, Distributed Computing
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