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 Paperpeer-review


    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 languageEnglish
    Title of host publicationLatin American High Performance Computing Conference
    Subtitle of host publicationCARLA 2019: High Performance Computing
    PublisherSpringer Open
    Number of pages16
    Volume1087 CCIS
    ISBN (Print)18650929 (ISSN); 9783030410049 (ISBN)
    Publication statusPublished - 2020
    Event6th Latin American High Performance Computing Conference - Turrialba, Costa Rica
    Duration: 25 Sep 201927 Sep 2019


    Conference6th Latin American High Performance Computing Conference
    Abbreviated titleCARLA 2019
    Country/TerritoryCosta Rica
    Internet address


    • 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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