Multi-objective Configuration of a Secured Distributed Cloud Data Storage

L.E. García-Hernández; A. Tchernykh; V. Miranda-López; M. Babenko; A. Avetisyan; R. Rivera-Rodriguez; G. Radchenko; C.J. Barrios-Hernandez; H. Castro; A.Y. Drozdov; Crespo-Marino J.L.; Meneses-Rojas E.

doi:10.1007/978-3-030-41005-6_6

Multi-objective Configuration of a Secured Distributed Cloud Data Storage

L.E. García-Hernández, A. Tchernykh, V. Miranda-López, M. Babenko, A. Avetisyan, R. Rivera-Rodriguez, G. Radchenko, C.J. Barrios-Hernandez, H. Castro, A.Y. Drozdov, Crespo-Marino J.L. (Editor), Meneses-Rojas E. (Editor)

Research output: Conference proceeding/Chapter in Book/Report/ › Conference Paper › peer-review

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. © 2020, Springer Nature Switzerland AG.

Original language	English
Title of host publication	Latin American High Performance Computing Conference
Subtitle of host publication	CARLA 2019: High Performance Computing
Publisher	Springer Open
Pages	78-93
Number of pages	16
Volume	1087 CCIS
ISBN (Print)	18650929 (ISSN); 9783030410049 (ISBN)
DOIs	https://doi.org/10.1007/978-3-030-41005-6_6
Publication status	Published - 2020
Event	6th Latin American High Performance Computing Conference - Turrialba, Costa Rica Duration: 25 Sept 2019 → 27 Sept 2019 http://carla2019.ccarla.org/

Conference

Conference	6th Latin American High Performance Computing Conference
Abbreviated title	CARLA 2019
Country/Territory	Costa Rica
City	Turrialba
Period	25/09/19 → 27/09/19
Internet address	http://carla2019.ccarla.org/

Keywords

Cloud storage
Genetic algorithm
Multi-objective optimization
Approximation algorithms
Error correction
Genetic algorithms
Multiobjective optimization
Numbering systems
Pareto principle
Cloud storages
Conflicting objectives
Error correction codes
Experimental analysis
Multi-objective genetic algorithm
Redundant residue number systems
Secret sharing schemes
Storage optimization
Digital storage

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10.1007/978-3-030-41005-6_6

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081172476&doi=10.1007%2f978-3-030-41005-6_6&partnerID=40&md5=b5a56900e2a40f2142d0ce72153d345a

Cite this

García-Hernández, L. E., Tchernykh, A., Miranda-López, V., Babenko, M., Avetisyan, A., Rivera-Rodriguez, R., Radchenko, G., Barrios-Hernandez, C. J., Castro, H., Drozdov, A. Y., J.L., C.-M. (Ed.), & E., M.-R. (Ed.) (2020). Multi-objective Configuration of a Secured Distributed Cloud Data Storage. In Latin American High Performance Computing Conference: CARLA 2019: High Performance Computing (Vol. 1087 CCIS, pp. 78-93). Springer Open. https://doi.org/10.1007/978-3-030-41005-6_6

@inproceedings{9d709cf5942643a8a96ade3d93f5957d,

title = "Multi-objective Configuration of a Secured Distributed Cloud Data Storage",

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. {\textcopyright} 2020, Springer Nature Switzerland AG.",

keywords = "Cloud storage, Genetic algorithm, Multi-objective optimization, Approximation algorithms, Error correction, Genetic algorithms, Multiobjective optimization, Numbering systems, Pareto principle, Cloud storages, Conflicting objectives, Error correction codes, Experimental analysis, Multi-objective genetic algorithm, Redundant residue number systems, Secret sharing schemes, Storage optimization, Digital storage",

author = "L.E. Garc{\'i}a-Hern{\'a}ndez and A. Tchernykh and V. Miranda-L{\'o}pez and M. Babenko and A. Avetisyan and R. Rivera-Rodriguez and G. Radchenko and C.J. Barrios-Hernandez and H. Castro and A.Y. Drozdov and Crespo-Marino J.L. and Meneses-Rojas E.",

note = "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.; 6th Latin American High Performance Computing Conference, CARLA 2019 ; Conference date: 25-09-2019 Through 27-09-2019",

year = "2020",

doi = "10.1007/978-3-030-41005-6_6",

language = "English",

isbn = "18650929 (ISSN); 9783030410049 (ISBN)",

volume = "1087 CCIS",

pages = "78--93",

booktitle = "Latin American High Performance Computing Conference",

publisher = "Springer Open",

address = "United Kingdom",

url = "http://carla2019.ccarla.org/",

}

García-Hernández, LE, Tchernykh, A, Miranda-López, V, Babenko, M, Avetisyan, A, Rivera-Rodriguez, R, Radchenko, G, Barrios-Hernandez, CJ, Castro, H, Drozdov, AY, J.L., C-M (ed.) & E., M-R (ed.) 2020, Multi-objective Configuration of a Secured Distributed Cloud Data Storage. in Latin American High Performance Computing Conference: CARLA 2019: High Performance Computing. vol. 1087 CCIS, Springer Open, pp. 78-93, 6th Latin American High Performance Computing Conference, Turrialba, Costa Rica, 25/09/19. https://doi.org/10.1007/978-3-030-41005-6_6

Multi-objective Configuration of a Secured Distributed Cloud Data Storage. / García-Hernández, L.E.; Tchernykh, A.; Miranda-López, V. et al.
Latin American High Performance Computing Conference: CARLA 2019: High Performance Computing. Vol. 1087 CCIS Springer Open, 2020. p. 78-93.

Research output: Conference proceeding/Chapter in Book/Report/ › Conference Paper › peer-review

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 -

Multi-objective Configuration of a Secured Distributed Cloud Data Storage

Abstract

Conference

Keywords

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