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English Information

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Authors
# Name
1 Wesley Ferreira(wesleyferreira@id.uff.br)
2 Liliane Kunstmann(lneves@cos.ufrj.br)
3 Raphael Garcia(raphaelgarcia@id.uff.br)
4 Marcos Bedo(marcosbedo@id.uff.br)
5 Aline Paes(alinepaes@ic.uff.br)
6 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 Babuji, Yadu N.; Woodard, Anna; Li, Zhuozhao; Katz, Daniel S.; Clifford, Ben; Kumar, Rohan; Lacinski, Lukasz; Chard, Ryan; Wilde, Michael; e Foster, Ian T. (2019). Parsl: Pervasive Parallel Programming in Python. In J. B. Weissman, A. R. Butt, & E. Smirni (Eds.), Proceedings of the 28th International Symposium on High-Performance Parallel and Distributed Computing (HPDC '19) (pp. 25–36). ACM.
2 de Oliveira, D., Ogasawara, E. S., Baião, F. A., & Mattoso, M. (2010). Scicumulus: A lightweight cloud middleware to explore many task computing paradigm in scientific workflows. In Proceedings of the IEEE International Conference on Cloud Computing (CLOUD’10) (pp. 378–385).
3 de Oliveira, D., Ocaña, K. A. C. S., Baião, F. A., e Mattoso, M. (2012). A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds. Journal of Grid Computing, 10(3):521–552.
4 Belhajjame, K., B’Far, R., Cheney, J., Coppens, S., Cresswell, S., Gil, Y., Groth, P., Klyne, G., Lebo, T., McCusker, J., Miles, S., Myers, J., Sahoo, S., Tilmes, C., Moreau, L., and Missier, P. (2013). Prov-dm: The prov data model. W3C Recommendation / Technical Report REC-prov-dm-20130430, World Wide Web Consortium. Editors: Luc Moreau and Paolo Missier.
5 de Oliveira, D. C. M., Liu, J., and Pacitti, E. (2019). Data-Intensive Workflow Manage- ment: For Clouds and Data-Intensive and Scalable Computing Environments. Synthe- sis Lectures on Data Management. Morgan & Claypool Publishers.
6 Deelman, E., da Silva, R. F., Vahi, K., Rynge, M., Mayani, R., Tanaka, R., Whitcup, W. R., and Livny, M. (2021). The pegasus workflow management system: Translatio- nal computer science in practice. J. Comput. Sci., 52:101200.
7 Ferreira, W., Kunstmann, L., Paes, A., Bedo, M., and de Oliveira, D. (2024). Akôflow: um middleware para execução de workflows científicos em múltiplos ambientes conteinerizados. In Anais do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 27–39, Florianópolis/SC. SBC.
8 Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for computational tasks: A survey. Computing in science & engineering, 10(3):11–21.
9 Kunstmann, L., Pina, D., Oliveira, L., Oliveira, D., e Mattoso, M. (2022). Provdeploy: explorando alternativas de conteinerização com proveniência para aplicações científicas com PAD. In Anais do XXIII Simpósio em Sistemas Computacionais de Alto Desempenho, páginas 49–60, Florianópolis/SC. SBC.
10 Kurtzer, G. M., Sochat, V., and Bauer, M. W. (2017). Singularity: Scientific containers for mobility of compute. PLOS ONE, 12(5):e0177459.
11 Ogasawara, E. S., de Oliveira, D., Valduriez, P., Dias, J., Porto, F., e Mattoso, M. (2011). An algebraic approach for data-centric scientific workflows. Proceedings of the VLDB Endowment, 4(12):1328–1339.
12 Sakellariou, Rizos; Zhao, H. H.; e Deelman, Ewa. (2009). Mapping Workflows on Grid Resources: Experiments with the Montage Workflow. In ERCIM Workshop Group on Grids (pp. 119–132)
13 Struhár, V., Behnam, M., Ashjaei, M., e Papadopoulos, A. V. (2020). Real-time containers: A survey. In Fog-IoT, volume 80 de OASIcs, páginas 7:1–7:9.