Multi-objective Configuration of a Secured Distributed Cloud Data Storage

  • Luis Enrique García-Hernández
  • Andrei TchernykhEmail author
  • Vanessa Miranda-López
  • Mikhail Babenko
  • Arutyun Avetisyan
  • Raul Rivera-Rodriguez
  • Gleb Radchenko
  • Carlos Jaime Barrios-Hernandez
  • Harold Castro
  • Alexander Yu. Drozdov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1087)


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.


Cloud storage Multi-objective optimization Genetic algorithm 



The work is partially supported by Russian Federation President Grant MK-341.2019.9.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.CICESE Research CenterEnsenadaMexico
  2. 2.North-Caucasus Federal UniversityStavropolRussia
  3. 3.Ivannikov Institute for System Programming RASMoscowRussia
  4. 4.South Ural State UniversityChelyabinskRussia
  5. 5.Universidad Industrial de SantanderBucaramangaColombia
  6. 6.Universidad de los AndesBogotáColombia
  7. 7.Moscow Institute of Physics and Technology (State University)MoscowRussia

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