Ensemble learning of run-time prediction models for data-intensive scientific workflows

Abstract

Workflow applications for in-silico experimentation involve the processing of large amounts of data. One of the core issues for the efficient management of such applications is the prediction of tasks performance. This paper proposes a novel approach that enables the construction models for predicting task’s running-times of data-intensive scientific workflows. 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. The proposed approach has been tested on Bioinformatics workflows for Gene Expressions Analysis over homogeneous and heterogeneous computing environments. Obtained results highlight the convenience of using ensemble models in comparison with single/standalone prediction models. Ensemble learning techniques permitted reductions of the prediction error up to 24.9% in comparison with single-model strategies.