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English Information

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Authors
# Name
1 Rodrigo Silva(rodrigo_prado@id.uff.br)
2 Wesley Ferreira(wesleyferreira@id.uff.br)
3 Esther Pacitti( Esther.Pacitti@inria.fr)
4 Yuri Frota(yuri@ic.uff.br)
5 Daniel de Oliveira(danielcmo@ic.uff.br)

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Reference
# Reference
1 Abazari, F. et al. (2019). Mows: multi-objective workflow scheduling in cloud computing based on heuristic algorithm. Sim. Mod. Pract. and Theory., 93
2 Abraham, S. (2023). The hpc container experience on the summit supercomputer. In PEARC’23, page 273–277
3 Branco-Jr., E. C., Monteiro, J. M., et al. (2016). A flexible mechanism for data confidentiality in cloud database scenarios. In ICEIS 2016, pages 359–368.
4 de Oliveira, D., Liu, J., and Pacitti, E. (2019). Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Morgan & Claypool
5 Ferreira, W. et al. (2024). AkôFlow: um Middleware para execução de Workflows científicos em múltiplos ambientes conteinerizados. In Proc. of the 39th SBBD. SBC.
6 Guerine, M. A., Stockinger, M. B., et al. (2019). A provenance-based heuristic for preserving results confidentiality in cloud-based scientific workflows. FGCS, 97:697–713
7 Javed, O. and Toor, S. (2021). An evaluation of container security vulnerability detection tools. ICCBDC ’21, page 95–101.
8 Rosseti, I., Ocaña, K., and de Oliveira, D. (2017). Towards preserving results confidentiality in cloud-based scientific workflows. WORKS ’17, New York, NY, USA. ACM.
9 Sakellariou, R. et al. (2009). Mapping workflows on grid resources: Experiments with the montage workflow. In ERCIM W. Group on Grids, pages 119–132
10 Shishido, H., Estrella, J. C., et al. (2018). Multi-objective optimization for workflow scheduling under task selection policies in clouds. In CEC, pages 1–8
11 Silva, R. et al. (2021). Análise de desempenho da distribuição de workflows científicos em nuvens com restrições de confidencialidade. In XX WPerformance, pages 37–48
12 Sujana, J. A. J. et al. (2019). Smart pso-based secured scheduling approaches for scientific workflows in cloud computing. Soft. Comp., 23(5):1745–1765
13 Tawfeek, M. A. et al. (2018). Service flow management with multi-objective constraints in heterogeneous computing. In ICCES, pages 258–263
14 Teylo, L. et al. (2017). A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds. FGCS, 76:1–17
15 Topcuoglu, H. et al. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE TPDS, 13(3):260–274