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dc.contributor.authorShukri, SE
dc.contributor.authorAl-Sayyed, R
dc.contributor.authorHudaib, A
dc.contributor.authorMirjalili, S
dc.description.abstractCloud computing is a trending technology that allows users to use computing resources remotely in a pay-per-use model. One of the main challenges in cloud computing environments is task scheduling, in which tasks should be scheduled efficiently to minimize execution time and cost while maximizing resources’ utilization. Many meta-heuristic algorithms are used for task scheduling in cloud environments in the literature such as Multi-Verse Optimizer (MVO) and Particle Swarm Optimization (PSO). In this paper, an Enhanced version of the Multi-Verse Optimizer (EMVO) is proposed as a superior task scheduler in this area. The proposed EMVO is compared with both original MVO and the PSO algorithms in cloud environments. The results show that EMVO substantially outperforms both MVO and PSO algorithms in terms of achieving minimized makespan time and increasing resources’ utilization.
dc.relation.ispartofjournalExpert Systems with Applications
dc.subject.fieldofresearchMathematical Sciences
dc.subject.fieldofresearchInformation and Computing Sciences
dc.titleEnhanced multi-verse optimizer for task scheduling in cloud computing environments
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationShukri, SE; Al-Sayyed, R; Hudaib, A; Mirjalili, S, Enhanced multi-verse optimizer for task scheduling in cloud computing environments, Expert Systems with Applications, 2020
gro.description.notepublicThis publication has been entered as advanced online version in Griffith Research Online.
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali

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