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dc.contributor.authorGhafir, Shabina
dc.contributor.authorAlam, M. Afshar
dc.contributor.authorSiddiqui, Farheen
dc.contributor.authorNaaz, Sameena
dc.contributor.authorSohail, Shahab Saquib
dc.contributor.authorMadsen, Dag Øivind
dc.date.accessioned2024-05-06T10:55:10Z
dc.date.available2024-05-06T10:55:10Z
dc.date.created2024-02-05T08:33:35Z
dc.date.issued2024
dc.identifier.citationGhafir, S., Alam, M. A., Siddiqui, F., Naaz, S., Sohail, S. S., & Madsen, D. Ø. (2024). Toward optimizing scientific workflow using multi-objective optimization in a cloud environment. Cogent Engineering, 11(1), Artikkel 2287303.en_US
dc.identifier.issn2331-1916
dc.identifier.urihttps://hdl.handle.net/11250/3129197
dc.description.abstractScientific workflows are a common and critical part of scientific computing, involving complex computations and oversized and distributed computing resources. Efficient workflow execution requires scheduling algorithms considering task dependencies, resource requirements, and deadlines. Cloud computing provides an innovative architecture for extensive heterogeneous computing services. However, scheduling hybrid cloud resources with deadline restrictions while observing QoS standards is an NP-complete task. Mapping workflow tasks to virtual machines and determining the optimal schedule order is a challenging aspect of cloud computing. By executing task requests on the most advantageous virtual machine in the resource pool, energy consumption, overall execution time, and computing costs can be reduced. This research aims to identify the best location to process applications using user’s demand and priority. A multi-objective genetic algorithm is proposed to achieve this objective, which considers conflicting objectives such as time, energy, cost, and deadline. The algorithm initializes an efficient ranking heuristic approach and predicts the earliest finish time (PEFT) using the Bayesian approach to improve the Pareto fronts. This approach enhances the VM migration of cloud-based tasks and optimizes the search space for conflicting objectives. Experimental findings show that the proposed approach reduces cost by 5–6% and time delay by 8% compared to existing approaches. The proposed approach offers an effective solution for scheduling scientific workflows on cloud computing resources while considering various QoS standards. The results demonstrate the potential of multi-objective genetic algorithms for optimizing workflow scheduling in cloud computing environments.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleToward optimizing scientific workflow using multi-objective optimization in a cloud environmenten_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2023 The Author(s).en_US
dc.source.volume11en_US
dc.source.journalCogent Engineeringen_US
dc.source.issue1en_US
dc.identifier.doihttps://doi.org/10.1080/23311916.2023.2287303
dc.identifier.cristin2243034
dc.source.articlenumber2287303en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal