Auto-scaling method in hybrid cloud for scientific applications
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Younsun | - |
dc.contributor.author | Choi, Jieun | - |
dc.contributor.author | Jeong, Sol | - |
dc.contributor.author | Kim, Yoonhee | - |
dc.date.available | 2021-02-22T10:52:48Z | - |
dc.date.issued | 2014-12 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/5756 | - |
dc.description.abstract | Scientists can ease to conduct large-scale scientific computational experiments over cloud environment according to an appearance of Science Clouds. Cloud computing enables applications to apply on-demand and scalable resources dynamically. It is necessary for Many Task Computing (MTC) to offer high performance resources in a long phase and certificate stable executions of applications even dramatic changes of vital status of physical resources. Auto-scaling on virtual machines provides integrated and efficient utilization of cloud resources. VM Auto-scaling schemes have been actively studied as effective resource management in order to utilize large-scale data center in a good shape. However, most of the existing auto-scaling methods just simply support CPU utilization and data transfer latency. It is needed to consider execution deadline or characteristics of an application. We propose an auto-scaling method, guaranteeing the execution of an application within deadline. It can handle two types of job patterns; Bag-of-Tasks jobs or workflow jobs. We simulate a variable index computation application in hybrid cloud environment. The results of the simulation show the method can dynamically allocate resources considering deadline. © 2014 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Auto-scaling method in hybrid cloud for scientific applications | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/APNOMS.2014.6996527 | - |
dc.identifier.scopusid | 2-s2.0-84941089916 | - |
dc.identifier.bibliographicCitation | APNOMS 2014 - 16th Asia-Pacific Network Operations and Management Symposium | - |
dc.citation.title | APNOMS 2014 - 16th Asia-Pacific Network Operations and Management Symposium | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Cloud computing | - |
dc.subject.keywordPlus | Data transfer | - |
dc.subject.keywordPlus | Distributed computer systems | - |
dc.subject.keywordPlus | Auto-scaling | - |
dc.subject.keywordPlus | Bag of tasks | - |
dc.subject.keywordPlus | Hybrid Cloud computing | - |
dc.subject.keywordPlus | Science clouds | - |
dc.subject.keywordPlus | Workflow | - |
dc.subject.keywordPlus | Information management | - |
dc.subject.keywordAuthor | Auto-scaling | - |
dc.subject.keywordAuthor | Bag-of-tasks | - |
dc.subject.keywordAuthor | Hybrid cloud computing | - |
dc.subject.keywordAuthor | Science cloud | - |
dc.subject.keywordAuthor | Workflow | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/6996527 | - |
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.