TY - GEN
T1 - Multi-objective Configuration of a Secured Distributed Cloud Data Storage
AU - García-Hernández, L.E.
AU - Tchernykh, A.
AU - Miranda-López, V.
AU - Babenko, M.
AU - Avetisyan, A.
AU - Rivera-Rodriguez, R.
AU - Radchenko, G.
AU - Barrios-Hernandez, C.J.
AU - Castro, H.
AU - Drozdov, A.Y.
A2 - J.L., Crespo-Marino
A2 - E., Meneses-Rojas
N1 - Conference code: 237659
Cited By :4
Export Date: 27 August 2021
Correspondence Address: Tchernykh, A.; CICESE Research CenterMexico; email: chenykh@cicese.mx
Funding details: Council on grants of the President of the Russian Federation, MK-341.2019.9
Funding text 1: Acknowledgments. The work is partially supported by Russian Federation President Grant MK-341.2019.9.
PY - 2020
Y1 - 2020
N2 - 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. © 2020, Springer Nature Switzerland AG.
AB - 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. © 2020, Springer Nature Switzerland AG.
KW - Cloud storage
KW - Genetic algorithm
KW - Multi-objective optimization
KW - Approximation algorithms
KW - Error correction
KW - Genetic algorithms
KW - Multiobjective optimization
KW - Numbering systems
KW - Pareto principle
KW - Cloud storages
KW - Conflicting objectives
KW - Error correction codes
KW - Experimental analysis
KW - Multi-objective genetic algorithm
KW - Redundant residue number systems
KW - Secret sharing schemes
KW - Storage optimization
KW - Digital storage
U2 - 10.1007/978-3-030-41005-6_6
DO - 10.1007/978-3-030-41005-6_6
M3 - Conference Paper
SN - 18650929 (ISSN); 9783030410049 (ISBN)
VL - 1087 CCIS
SP - 78
EP - 93
BT - Latin American High Performance Computing Conference
PB - Springer Open
T2 - 6th Latin American High Performance Computing Conference
Y2 - 25 September 2019 through 27 September 2019
ER -