Auto-scaling of virtual resources for scientific workflows on hybrid clouds
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahn, Younsun | - |
dc.contributor.author | Kim, Yoonhee | - |
dc.date.available | 2021-02-22T12:01:50Z | - |
dc.date.issued | 2014-06 | - |
dc.identifier.uri | https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/11034 | - |
dc.description.abstract | Cloud computing technology enables applications to employ scalable resources dynamically. Scientists can promote large-scale scientific computational experiments over cloud environment. It is essential for many-task-computing (MTC) to certificate stable executions of applications even rapid changes of vital status of physical resources and furnish high performance resources in a long period. Auto-scaling with virtualization provides efficient and integrated cloud resource utilization. Auto-scaling issues have been actively studied as effective resource management in order to utilize large-scale data center in a good shape but most of the auto-scaling methods just easily support performance metrics such as CPU utilization and data transfer latency but seldom consider execution deadline or characteristics of an application. We propose an auto-scaling method that finishes all tasks by user specified deadline. We accomplish our goal by dynamically allocating VMs to maximize resource utilization while meeting a deadline and considering task dependency and data transfer time in workflow application. We have evaluated our auto-scaling method with protein annotation workflow application which tasks are specified as a workflow in hybrid cloud environment. The results of a simulation show the method performs automatically resource allocation actually needed satisfying deadline constraints. Copyright © 2014 ACM. | - |
dc.format.extent | 466 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Auto-scaling of virtual resources for scientific workflows on hybrid clouds | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1145/2608029.2608036 | - |
dc.identifier.scopusid | 2-s2.0-84904536700 | - |
dc.identifier.bibliographicCitation | ScienceCloud 2014 - Proceedings of the 2014 ACM International Workshop on Scientific Cloud Computing, Co-located with HPDC 2014, pp 47 - 512 | - |
dc.citation.title | ScienceCloud 2014 - Proceedings of the 2014 ACM International Workshop on Scientific Cloud Computing, Co-located with HPDC 2014 | - |
dc.citation.startPage | 47 | - |
dc.citation.endPage | 512 | - |
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 | Resource allocation | - |
dc.subject.keywordPlus | Auto-scaling | - |
dc.subject.keywordPlus | Cloud computing technologies | - |
dc.subject.keywordPlus | Computational experiment | - |
dc.subject.keywordPlus | Hybrid | - |
dc.subject.keywordPlus | Resource utilizations | - |
dc.subject.keywordPlus | Scientific workflows | - |
dc.subject.keywordPlus | Workflow | - |
dc.subject.keywordPlus | Workflow applications | - |
dc.subject.keywordPlus | Information management | - |
dc.subject.keywordAuthor | Auto-scaling | - |
dc.subject.keywordAuthor | Cloud computing | - |
dc.subject.keywordAuthor | Hybrid | - |
dc.subject.keywordAuthor | Workflow | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/2608029.2608036 | - |
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