A QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithm

dc.authoridJafari Navimipour, Nima/0000-0002-5514-5536
dc.authorwosidJafari Navimipour, Nima/AAF-5662-2021
dc.contributor.authorMilan, Sara Tabagchi
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorBavil, Hamed Lohi
dc.contributor.authorYalcin, Senay
dc.date.accessioned2023-10-19T15:12:51Z
dc.date.available2023-10-19T15:12:51Z
dc.date.issued2023
dc.department-temp[Milan, Sara Tabagchi] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Navimipour, Nima Jafari] Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu, Taiwan; [Bavil, Hamed Lohi] Univ Appl Sci & Technol, Dept Comp Engn, Tabriz, Iran; [Yalcin, Senay] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkiyeen_US
dc.description.abstractNowadays, high energy amount is being wasted by computing servers and personal electronic devices, which produce a high amount of carbon dioxide. Thus, it is required to decrease energy usage and pollution. Many applications are utilised by green computing to save energy. Scheduling of tasks acts as an important process to reach the mentioned goals. It is worth stating that the vital characteristic of task scheduling in green clouds is the load balancing of tasks on virtual machines. Efficient load balancing moves tasks from overloaded to underloaded virtual machines to maintain the Quality of Service (QoS). This issue is an NP-complete problem, so this research suggests a new technique based on the behavioural structure of artificial bee behaviour. This method aims to improve QoS while lowering energy usage in green computing. In addition, the honey bees are considered the removed tasks from overloaded virtual machines and a candidate for migrating selected tasks with the lowest priority. The CloudSim testing findings demonstrate that the technique is successful in QoS, makespan, and energy usage compared to other ways.en_US
dc.identifier.citation0
dc.identifier.doi10.1080/0952813X.2023.2188490en_US
dc.identifier.issn0952-813X
dc.identifier.issn1362-3079
dc.identifier.scopus2-s2.0-85150802727en_US
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1080/0952813X.2023.2188490
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5548
dc.identifier.wosWOS:000953152900001en_US
dc.identifier.wosqualityN/A
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofJournal of Experimental & Theoretical Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectScheduling AlgorithmEn_Us
dc.subjectAllocationEn_Us
dc.subjectFrameworkEn_Us
dc.subjectSystemEn_Us
dc.subjectTasksEn_Us
dc.subject5gEn_Us
dc.subjectScheduling Algorithm
dc.subjectAllocation
dc.subjectFramework
dc.subjectGreen computingen_US
dc.subjectSystem
dc.subjectload balancingen_US
dc.subjectTasks
dc.subjectartificial bee colonyen_US
dc.subject5g
dc.subjectcloud computingen_US
dc.titleA QoS-based technique for load balancing in green cloud computing using an artificial bee colony algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication

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