Abstract
Cloud storage is one of the most popular models of cloud computing. It benefits from a shared set of configurable resources without limitations of local data storage infrastructures. However, it brings several cybersecurity issues. In this work, we address the methods of mitigating risks of confidentiality, integrity, availability, information leakage associated with the information loss/change, technical failures, and denial of access. We rely on a configurable secret sharing scheme and error correction codes based on the Redundant Residue Number System (RRNS). To dynamically configure RRNS parameters to cope with different objective preferences, workloads, and cloud properties, we take into account several conflicting objectives: probability of information loss/change, extraction time, and data redundancy. We propose an approach based on a genetic algorithm that is effective for multi-objective optimization. We implement NSGA-II, SPEA2, and MOCell, using the JMetal 5.6 framework. We provide their experimental analysis using eleven real data cloud storage providers. We show that MOCell algorithm demonstrates best results obtaining a better Pareto optimal front approximation and quality indicators such as inverted generational distance, additive epsilon indicator, and hypervolume. We conclude that multi-objective genetic algorithms could be efficiently used for storage optimization and adaptation in a non-stationary multi-cloud environment.
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The work is partially supported by Russian Federation President Grant MK-341.2019.9.
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García-Hernández, L.E. et al. (2020). Multi-objective Configuration of a Secured Distributed Cloud Data Storage. In: Crespo-Mariño, J., Meneses-Rojas, E. (eds) High Performance Computing. CARLA 2019. Communications in Computer and Information Science, vol 1087. Springer, Cham. https://doi.org/10.1007/978-3-030-41005-6_6
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DOI: https://doi.org/10.1007/978-3-030-41005-6_6
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