Please use this identifier to cite or link to this item:
https://dspace.ncfu.ru/handle/20.500.12258/11906
Title: | Multi-objective configuration of a secured distributed cloud data storage |
Authors: | Babenko, M. G. Бабенко, М. Г. |
Keywords: | Cloud storage;Genetic algorithm;Multi-objective optimization;Approximation algorithms;Error correction;Digital storage |
Issue Date: | 2020 |
Publisher: | Springer |
Citation: | 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. Multi-objective Configuration of a Secured Distributed Cloud Data Storage // Communications in Computer and Information Science. - 2020. - Volume 1087, CCIS. - Pages 78-93 |
Series/Report no.: | Communications in Computer and Information Science |
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 |
URI: | http://hdl.handle.net/20.500.12258/11906 |
Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
Files in This Item:
File | Description | Size | Format | |
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scopusresults 1225 .pdf Restricted Access | 64.65 kB | Adobe PDF | View/Open | |
WoS 1107 .pdf Restricted Access | 830.75 kB | Adobe PDF | View/Open |
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