Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Sungju | - |
dc.contributor.author | Jeong, Taikyeong | - |
dc.date.accessioned | 2021-09-03T19:40:49Z | - |
dc.date.available | 2021-09-03T19:40:49Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2016-10 | - |
dc.identifier.issn | 2073-8994 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/87416 | - |
dc.description.abstract | A fundamental key for enterprise users is a cloud-based parameter-driven statistical service and it has become a substantial impact on companies worldwide. In this paper, we demonstrate the statistical analysis for some certain criteria that are related to data and applied to the cloud server for a comparison of results. In addition, we present a statistical analysis and cloud-based resource allocation method for a heterogeneous platform environment by performing a data and information analysis with consideration of the application workload and the server capacity, and subsequently propose a service prediction model using a polynomial regression model. In particular, our aim is to provide stable service in a given large-scale enterprise cloud computing environment. The virtual machines (VMs) for cloud-based services are assigned to each server with a special methodology to satisfy the uniform utilization distribution model. It is also implemented between users and the platform, which is a main idea of our cloud computing system. Based on the experimental results, we confirm that our prediction model can provide sufficient resources for statistical services to large-scale users while satisfying the uniform utilization distribution. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Cloud-Based Parameter-Driven Statistical Services and Resource Allocation in a Heterogeneous Platform on Enterprise Environment | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Sungju | - |
dc.identifier.doi | 10.3390/sym8100103 | - |
dc.identifier.scopusid | 2-s2.0-84995970168 | - |
dc.identifier.wosid | 000388586600005 | - |
dc.identifier.bibliographicCitation | SYMMETRY-BASEL, v.8, no.10 | - |
dc.relation.isPartOf | SYMMETRY-BASEL | - |
dc.citation.title | SYMMETRY-BASEL | - |
dc.citation.volume | 8 | - |
dc.citation.number | 10 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordAuthor | cloud computing environments | - |
dc.subject.keywordAuthor | data analysis | - |
dc.subject.keywordAuthor | statistical analysis | - |
dc.subject.keywordAuthor | data mining | - |
dc.subject.keywordAuthor | heterogeneous platform | - |
dc.subject.keywordAuthor | enterprise system | - |
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