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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

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dc.contributor.authorRaj, R. Joshua Samuel-
dc.contributor.authorVaralatchoumy, M.-
dc.contributor.authorJosephine, V. L. Helen-
dc.contributor.authorJegatheesan, A.-
dc.contributor.authorKadry, Seifedine-
dc.contributor.authorMeqdad, Maytham N.-
dc.contributor.authorNam, Yunyoung-
dc.date.accessioned2022-02-03T01:40:19Z-
dc.date.available2022-02-03T01:40:19Z-
dc.date.issued2022-01-
dc.identifier.issn1546-2218-
dc.identifier.issn1546-2226-
dc.identifier.urihttps://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20292-
dc.description.abstractInternet of Things (IoT) is transforming the technical setting of conventional systems and finds applicability in smart cities, smart health -care, smart industry, etc. In addition, the application areas relating to the IoT enabled models are resource-limited and necessitate crisp responses, low latencies, and high bandwidth, which are beyond their abilities. Cloud com-puting (CC) is treated as a resource-rich solution to the above mentioned challenges. But the intrinsic high latency of CC makes it nonviable. The longer latency degrades the outcome of IoT based smart systems. CC is an emergent dispersed, inexpensive computing pattern with massive assembly of heteroge-neous autonomous systems. The effective use of task scheduling minimizes the energy utilization of the cloud infrastructure and rises the income of service providers by the minimization of the processing time of the user job. With this motivation, this paper presents an intelligent Chaotic Artificial Immune Optimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabled cloud environment. The proposed CAIOA-RS algorithm solves the issue of resource allocation in the IoT enabled cloud environment. It also satisfies the makespan by carrying out the optimum task scheduling process with the distinct strategies of incoming tasks. The design of CAIOA-RS technique incorporates the concept of chaotic maps into the conventional AIOA to enhance its performance. A series of experiments were carried out on the CloudSim platform. The simulation results demonstrate that the CAIOA-RS technique indicates that the proposed model outperforms the original version, as well as other heuristics and metaheuristics.-
dc.format.extent15-
dc.language영어-
dc.language.isoENG-
dc.publisherTech Science Press-
dc.titleEvolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.32604/cmc.2022.021859-
dc.identifier.scopusid2-s2.0-85118683043-
dc.identifier.wosid000717553300019-
dc.identifier.bibliographicCitationComputers, Materials and Continua, v.71, no.1, pp 1095 - 1109-
dc.citation.titleComputers, Materials and Continua-
dc.citation.volume71-
dc.citation.number1-
dc.citation.startPage1095-
dc.citation.endPage1109-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.subject.keywordAuthorInternet of things-
dc.subject.keywordAuthorcloud computing-
dc.subject.keywordAuthortask scheduling-
dc.subject.keywordAuthormeta-heuristics-
dc.subject.keywordAuthorresource allocation-
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