A divide and conquer based development of gray wolf optimizer and its application in data replication problem in distributed systems

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dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridBouyer, Asgarali/0000-0002-4808-2856;
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidBouyer, Asgarali/IYS-5116-2023
dc.authorwosidBouyer, Asgarali/JOZ-6483-2023
dc.authorwosidFan, Wenguang/Q-4743-2018
dc.contributor.authorFan, Wenguang
dc.contributor.authorArasteh, Bahman
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorMajidnezhad, Vahid
dc.date.accessioned2024-05-19T14:40:55Z
dc.date.available2024-05-19T14:40:55Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractOne of the main problems of big distributed systems, like IoT, is the high access time to data objects. Replicating the data objects on various servers is a traditional strategy. Replica placement, which can be implemented statically or dynamically, is generally crucial to the effectiveness of distributed systems. Producing the minimum number of data copies and placing them on appropriate servers to minimize access time is an NP-complete optimization problem. Various heuristic techniques for efficient replica placement in distributed systems have been proposed. The main objectives of this research are to decrease the cost of data processing operations, decrease the number of copies, and improve the accessibility of the data objects. In this study, a discretized and group-based gray wolf optimization algorithm with swarm and evolutionary features was developed for the replica placement problem. The proposed algorithm includes swarm and evolutionary features and divides the wolves' population into subgroups, and each subgroup was locally searched in a different solution space. According to experiments conducted on the standard benchmark dataset, the suggested method provides about a 40% reduction in the data access time with about five replicas. Also, the reliability of the suggested method during different executions is considerably higher than the previous methods.en_US
dc.description.sponsorshipExploration and Practice of Cultivating Skilled Talents [2021jyxm0234]en_US
dc.description.sponsorshipExploration and Practice of Cultivating Skilled Talents through Modern Apprenticeship System Based on Project Based Teaching+2021jyxm0234.en_US
dc.identifier.doi10.1007/s11227-023-05401-1
dc.identifier.endpage19430en_US
dc.identifier.issn0920-8542
dc.identifier.issn1573-0484
dc.identifier.issue17en_US
dc.identifier.scopus2-s2.0-85160403438en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage19396en_US
dc.identifier.urihttps://doi.org10.1007/s11227-023-05401-1
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5035
dc.identifier.volume79en_US
dc.identifier.wosWOS:000995778700005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Supercomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDistributed Systemsen_US
dc.subjectData Access Timeen_US
dc.subjectReplica Placementen_US
dc.subjectGroup-Based Gray Wolf Optimizationen_US
dc.subjectStabilityen_US
dc.subjectNumber Of Replicasen_US
dc.titleA divide and conquer based development of gray wolf optimizer and its application in data replication problem in distributed systemsen_US
dc.typeArticleen_US

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