Ensemble Learning of Run-Time Prediction Models for Data-Intensive Scientific Workflows - High Performance Computing - page 96

Acknowledgements. This research is supported by the ANPCyT through
project No. PICT-2012-2731. This study is also founded by the Argentine Min-
istry of Science (MINCyT) and the Czech Ministry of Education, Youth and
Sports (MEYS) through projects Nos. RC0904 and MEB111005. The financial
support from SeCTyP-UNCuyo through project No. M004 is gratefully acknowl-
edged. The first author wants to thank CONICET for the granted fellowship.
Finally we want to thank the anonymous reviewers who helped improving the
quality of this paper.
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