A Heuristic for Executing Workflows with Confidentiality Constraints in Containerized Environments
Abstract
Containerized environments are ideal for running scientific workflows because they offer a flexible and easily instantiated setting. Although there are solutions for executing workflows in containerized environments, these were not designed to handle scientific workflows, especially those with confidentiality requirements. Non-compliance with these requirements allows malicious users to infer unpublished results or the workflow structure. Data dispersion and encryption can be adopted in this context, but not independently of workflow scaling, as this can increase the total execution time or the associated financial cost. This paper presents Okinawa, a heuristic for executing workflows in containerized environments with confidentiality constraints.
Keywords:
Workflow, Scheduling, Confidentiality, Containers, Cloud Computing
References
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Sakellariou, R. et al. (2009). Mapping workflows on grid resources: Experiments with the montage workflow. In ERCIM W. Group on Grids, pages 119–132.
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.
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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.
Tawfeek, M. A. et al. (2018). Service flow management with multi-objective constraints in heterogeneous computing. In ICCES, pages 258–263.
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.
Topcuoglu, H. et al. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE TPDS, 13(3):260–274.
Abraham, S. (2023). The hpc container experience on the summit supercomputer. In PEARC ’23, page 273–277.
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.
de Oliveira, D., Liu, J., and Pacitti, E. (2019). Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Morgan & Claypool.
Ferreira, W. et al. (2024). AkôFlow: um Middleware para execução de Workflows científicos em múltiplos ambientes conteinerizados. In Proc. of the 39th SBBD. SBC.
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.
Javed, O. and Toor, S. (2021). An evaluation of container security vulnerability detection tools. ICCBDC ’21, page 95–101.
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.
Sakellariou, R. et al. (2009). Mapping workflows on grid resources: Experiments with the montage workflow. In ERCIM W. Group on Grids, pages 119–132.
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.
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.
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.
Tawfeek, M. A. et al. (2018). Service flow management with multi-objective constraints in heterogeneous computing. In ICCES, pages 258–263.
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.
Topcuoglu, H. et al. (2002). Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE TPDS, 13(3):260–274.
Published
2024-10-14
How to Cite
SILVA, Rodrigo A. P.; FERREIRA, Wesley; PACITTI, Esther; FROTA, Yuri; DE OLIVEIRA, Daniel.
A Heuristic for Executing Workflows with Confidentiality Constraints in Containerized Environments. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 536-548.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240418.
