Ensemble learning of runtime prediction models for gene-expression analysis workflows

The adequate management of scientific workflow applications strongly depends on the availability of accurate performance models of sub-tasks. Numerous approaches use machine learning to generate such models autonomously, thus alleviating the human effort associated to this process. However, these standalone models may lack robustness, leading to a decay on the quality of information provided to workflow systems on top. This paper presents a novel approach for learning ensemble prediction models of tasks runtime. The ensemble-learning method entitled bootstrap aggregating (bagging) is used to produce robust ensembles of M5P regression trees of better predictive performance than could be achieved by standalone models. Our approach has been tested on gene expression analysis workflows. The results show that the ensemble method leads to significant prediction-error reductions when compared with learned standalone models. This is the first initiative using ensemble learning for generating performance prediction models. These promising results encourage further research in this direction.