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dc.contributor.authorAbdulhamid, Shafi’i Muhammad-
dc.contributor.authorAbd Latiff, Muhammad Shafie-
dc.contributor.authorAbdul—Salaam, Gaddafi-
dc.contributor.authorMadni, Syed Hamid Hussain-
dc.date.accessioned2021-07-13T09:42:45Z-
dc.date.available2021-07-13T09:42:45Z-
dc.date.issued2017-04-06-
dc.identifier.citationhttps://doi.org/10.1371/journal.pone.0158102en_US
dc.identifier.issn1932-6203-
dc.identifier.urihttp://repository.futminna.edu.ng:8080/jspui/handle/123456789/8950-
dc.description.abstractCloud computing system is a huge cluster of interconnected servers residing in a datacenterand dynamically provisioned to clients on-demand via a front-end interface. Scientific appli-cations scheduling in the cloud computing environment is identified as NP-hard problemdue to the dynamic nature of heterogeneous resources. Recently, a number of metaheuris-tics optimization schemes have been applied to address the challenges of applicationsscheduling in the cloud system, without much emphasis on the issue of secure globalscheduling. In this paper, scientific applications scheduling techniques using the GlobalLeague Championship Algorithm (GBLCA) optimization technique is first presented forglobal task scheduling in the cloud environment. The experiment is carried out using Cloud-Sim simulator. The experimental results show that, the proposed GBLCA technique pro-duced remarkable performance improvement rate on the makespan that ranges between14.44% to 46.41%. It also shows significant reduction in the time taken to securely scheduleapplications as parametrically measured in terms of the response time. In view of the experi-mental results, the proposed technique provides better-quality scheduling solution that issuitable for scientific applications task execution in the Cloud Computing environment thanthe MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) schedul-ing techniques.en_US
dc.language.isoenen_US
dc.publisherPloS oneen_US
dc.relation.ispartofseries11(7): e0158102;-
dc.subjectCloud computingen_US
dc.subjectSoft Computingen_US
dc.subjectMeta-heuristicen_US
dc.subjectComputer privacyen_US
dc.titleOptimal Resource Scheduling for IaaS Cloud Computing using Cuckoo Search Algorithmen_US
dc.typeArticleen_US
Appears in Collections:Cyber Security Science

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