A Hybrid Cloud Resource Clustering Method Using Analysis of Application Characteristics
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Yoori | - |
dc.contributor.author | Kim, Yoonhee | - |
dc.date.available | 2021-02-22T11:11:43Z | - |
dc.date.issued | 2017-10 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/8116 | - |
dc.description.abstract | With the development of cloud computing technology, there are many scientists who want to perform their experiments in cloud environments. Because of the pay-per-use method, it is cost-optimal for scientists to only pay for the cloud services needed for their experiments. However, selection of suitable resources is difficult because they are composed of various characteristics. Therefore, a method of classification is needed to effectively utilize cloud resources. Static classification of a resource can derive inaccurate results, while scientists submit various experiment intentions and requirements. Thus, a dynamic resource-clustering method is needed to accurately determine application characteristics and scientists' requirements. In this paper, a resource-clustering analysis, which considers application characteristics in a hybrid cloud environment is proposed. The resource clustering analysis applies a self-organizing map and the k-means algorithm to cluster similar resources dynamically. Performance is verified by comparing the proposed clustering method with other studies' resource classification methods. Results show that the proposed method can classify similar resource cluster reflecting application characteristics. © 2017 IEEE. | - |
dc.format.extent | 6 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | A Hybrid Cloud Resource Clustering Method Using Analysis of Application Characteristics | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/FAS-W.2017.162 | - |
dc.identifier.scopusid | 2-s2.0-85035193270 | - |
dc.identifier.bibliographicCitation | Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017, pp 295 - 300 | - |
dc.citation.title | Proceedings - 2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems, FAS*W 2017 | - |
dc.citation.startPage | 295 | - |
dc.citation.endPage | 300 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Conformal mapping | - |
dc.subject.keywordPlus | Self organizing maps | - |
dc.subject.keywordPlus | Cloud computing technologies | - |
dc.subject.keywordPlus | Cloud environments | - |
dc.subject.keywordPlus | Clustering analysis | - |
dc.subject.keywordPlus | Clustering methods | - |
dc.subject.keywordPlus | Dynamic resources | - |
dc.subject.keywordPlus | Hybrid clouds | - |
dc.subject.keywordPlus | Resource classification | - |
dc.subject.keywordPlus | Static classification | - |
dc.subject.keywordPlus | Cluster analysis | - |
dc.subject.keywordAuthor | cluster analysis | - |
dc.subject.keywordAuthor | dynamic resource clustering | - |
dc.subject.keywordAuthor | hybrid cloud | - |
dc.subject.keywordAuthor | self-organizing map | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8064138 | - |
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