Performance Analysis of the Distribution of Scientific Workflows in Computer Clouds with Confidentiality Constraints

• Rodrigo A. P. Silva Fluminense Federal University http://orcid.org/0000-0001-6683-2684
• Esther Pacitti University of Montpellier
• Yuri Frota Fluminense Federal University
• Daniel de Oliveira Fluminense Federal University

DOI: https://doi.org/10.5753/wperformance.2021.15721

Abstract

Clouds provide an on-demand environment that allows scientists to migrate their local experiments to an elastic environment. Experiments are modeled as scientific workflows, and many of them are computing and data-intensive. The storage of these data is a concern, as confidentiality can be compromised. Malicious users may infer knowledge of the results and structure of workflows. Data dispersion and encryption can be adopted to increase confidentiality, but these mechanisms cannot be adopted uncoupled from workflow scheduling, at the risk of increasing execution time and financial costs. In this paper, we present SaFER (workflow Scheduling with conFidEntity pRoblem), a scheduling approach that considers data confidentiality constraints.

Keywords: scheduling, conflict graph, dispersion plan, confidentiality, safer

References

Abazari, F., Analoui, M., Takabi, H., e Fu, S. (2019). Mows: multi-objective workflow scheduling in cloud computing based on heuristic algorithm.Simulation Modelling Practice and Theory, 93:119–132.

Branco-Jr., E. C., Monteiro, J. M., Reis, R., e Machado, J. C. (2016). A new mechanism to preserving data confidentiality in cloud database scenarios. InICEIS, volume 291,pages 261–283. Springer.

de Oliveira, D., Liu, J., e Pacitti, E. (2019). Data-Intensive Workflow Management: For Clouds and Data-Intensive and Scalable Computing Environments. Synthesis Lectures on Data Management. Morgan & Claypool Publishers.

de Oliveira, D., Ogasawara, E. S., Baião, F. A., e Mattoso, M. (2010). Scicumulus: A lightweight cloud middleware to explore many task computing paradigm in scientific workflows. In IEEE CLOUD 2010, Miami, pages 378–385.

Deelman, E., Vahi, K., Juve, G., Rynge, M., Callaghan, S., Maechling, P., Mayani, R.,Chen, W., da Silva, R. F., Livny, M., e Wenger, R. K. (2015). Pegasus, a workflow management system for science automation.Future Generation Comp. Syst., 46:17–35.

Ennajjar, I., Tabii, Y., e Benkaddour, A. (2017). Securing data in cloud computing by classification. BDCA’17, New York, NY, USA. ACM.

Freire, J., Koop, D., Santos, E., e Silva, C. T. (2008). Provenance for Computational Tasks: A Survey. Computing in Science & Engineering, pages 20–30.

Gendreau, M., Potvin, J.-Y., et al. (2010). Handbook of metaheuristics, volume 2. Springer.

González, L. M. V., Rodero-Merino, L., Caceres, J., e Lindner, M. A. (2009). A break in the clouds: towards a cloud definition.Computer Communication Review, 39(1):50–55.

Guerine, M., Stockinger, M. B., Rosseti, I., Simonetti, L. G., Ocaña, K. A., Plastino, A.,e de Oliveira, D. (2019). A provenance-based heuristic for preserving results confidentiality in cloud-based scientific workflows. Future Generation Computer Systems, 97:697 – 713.

Lee, K., Paton, N. W., Sakellariou, R., Deelman, E., Fernandes, A. A. A., e Mehta, G. (2009). Adaptive workflow processing and execution in pegasus. CCPE, 21(16):1965–1981.

Liu, J., Pacitti, E., Valduriez, P., e Mattoso, M. (2015). A survey of data-intensive scientific workflow management.J. Grid Comput., 13(4):457–493.

Shishido, H. Y., Estrella, J. C., e Toledo, C. F. M. (2018). Multi-objective optimizationfor workflow scheduling under task selection policies in clouds. InCEC, pages 1–8. IEEE.

Sujana, J. A. J., Revathi, T., Priya, T. S., e Muneeswaran, K. (2019). Smart pso-based secured scheduling approaches for scientific workflows in cloud computing. Soft. Comp., 23(5):1745–1765.

Tawfeek, M. A. e AbdulHamed, A. A. (2018). Service flow management with multi-objective constraints in heterogeneous computing. InICCES, pages 258–263. IEEE.

Teylo, L., de Paula Junior, U., Frota, Y., de Oliveira, D., e de A. Drummond, L. M.(2017). A hybrid evolutionary algorithm for task scheduling and data assignment of data-intensive scientific workflows on clouds. Future Gener. Comput. Syst., 76:1–17.