M. Atkinson, S. Gesing, J. Montagnat, and I. Taylor, Scientific workflows: Past, present and future, Future Generation Computer Systems, vol.75, pp.216-227, 2017.
DOI : 10.1016/j.future.2017.05.041
URL : https://hal.archives-ouvertes.fr/hal-01544818

E. Deelman, T. Peterka, I. Altintas, C. D. Carothers, K. Kleese-van-dam et al., The future of scientific workflows, The International Journal of High Performance Computing Applications, vol.38, 2017.
DOI : 10.1109/eScience.2014.9

J. Dias, G. Guerra, F. Rochinha, A. L. Coutinho, P. Valduriez et al., Data-centric iteration in dynamic workflows, Future Generation Computer Systems, vol.46, issue.C, pp.46114-126, 2015.
DOI : 10.1016/j.future.2014.10.021
URL : https://hal.archives-ouvertes.fr/lirmm-01073638

F. Da-silva, R. Filgueira, R. Pietri, I. Jiang, M. Sakellariou et al., A characterization of workflow management systems for extreme-scale applications, Future Generation Computer Systems, vol.75, pp.228-238, 2017.
DOI : 10.1016/j.future.2017.02.026

. Github and . Dfadapter-repository, Available at: https://github

M. Mattoso, J. Dias, K. A. Ocaña, E. Ogasawara, F. Costa et al., Dynamic steering of HPC scientific workflows: A survey, Future Generation Computer Systems, vol.46, pp.100-113, 2015.
DOI : 10.1016/j.future.2014.11.017

H. A. Nguyen, D. Abramson, T. Kiporous, A. Janke, and G. Galloway, WorkWays: interacting with scientific workflows, Concurrency and Computation: Practice and Experience, vol.219, issue.6, pp.21-24, 2014.
DOI : 10.1007/978-0-387-68628-8

I. Pouya, S. Pronk, M. Lundborg, and E. Lindahl, Copernicus, a hybrid dataflow and peer-to-peer scientific computing platform for efficient large-scale ensemble sampling, Future Generation Computer Systems, vol.71, pp.18-31, 2017.
DOI : 10.1016/j.future.2016.11.004