A resource recommendation method based on dynamic cluster analysis of application characteristics
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, Yoori | - |
dc.contributor.author | Kim, Yoonhee | - |
dc.date.available | 2021-02-22T06:45:48Z | - |
dc.date.issued | 2019-03 | - |
dc.identifier.issn | 1386-7857 | - |
dc.identifier.issn | 1573-7543 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/3749 | - |
dc.description.abstract | With the development of cloud computing technology, many scientists 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 take advantage of 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. A cost-effective resource recommendation service is also needed. In this paper, a resource-clustering analysis, which considers application characteristics, and a cost-effective recommendation method in a hybrid cloud environment are proposed. The resource clustering analysis applies a self-organizing map and the k-means algorithm to cluster similar resources dynamically. In addition, the cost-effective resource recommendation method applies an efficiency metric based on application-aware resource clustering. 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 and recommend cost-effective resources. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer New York LLC | - |
dc.title | A resource recommendation method based on dynamic cluster analysis of application characteristics | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1007/s10586-018-2829-0 | - |
dc.identifier.scopusid | 2-s2.0-85050622520 | - |
dc.identifier.wosid | 000460300400011 | - |
dc.identifier.bibliographicCitation | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, v.22, no.1, pp 175 - 184 | - |
dc.citation.title | CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 175 | - |
dc.citation.endPage | 184 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
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://link.springer.com/article/10.1007%2Fs10586-018-2829-0 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Sookmyung Women's University. Cheongpa-ro 47-gil 100 (Cheongpa-dong 2ga), Yongsan-gu, Seoul, 04310, Korea02-710-9127
Copyright©Sookmyung Women's University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.