Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment.
Cloud computing has emerged as a highly cost-effective computation paradigm for IT enterprise applications, scientific computing, and personal data management. Because cloud services are provided by machines of various capabilities, performance, power, and thermal characteristics, it is challenging...
| Publicado en: | Computer (00189162) Vol. 44; no. 8; p. 55 |
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| Autores principales: | , |
| Formato: | Artículo |
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IEEE
Aug2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=64345120&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 64345120 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00189162 PUT jtl: Computer (00189162) issn: 00189162 maglogo: N pubinfo: dt: Aug2011 vid: 44 iid: 8 pid: 13605 pub: IEEE artinfo: ui: 64345120 10.1109/MC.2011.96 ppf: 55 ppct: 0 formats: tig: atl: Using Mathematical Modeling in Provisioning a Heterogeneous Cloud Computing Environment. aug: au: Yeo, Sungkap Lee, Hsien-Hsin affil: Georgia Institute of Technology su: Cloud computing Mathematical models Distributed computing High performance computing Simulation methods & models sug: subj: Cloud computing Mathematical models Distributed computing High performance computing Simulation methods & models keyword: Computational modeling Cost benefit analysis Mathematical modeling Peer to peer computing Program processors Time factors Virtual machining ab: Cloud computing has emerged as a highly cost-effective computation paradigm for IT enterprise applications, scientific computing, and personal data management. Because cloud services are provided by machines of various capabilities, performance, power, and thermal characteristics, it is challenging for providers to understand their cost effectiveness when deploying their systems. This article analyzes a parallelizable task in a heterogeneous cloud infrastructure with mathematical models to evaluate the energy and performance trade-off. As the authors show, to achieve the optimal performance per utility, the slowest node's response time should be no more than three times that of the fastest node. The theoretical analysis presented can be used to guide allocation, deployment, and upgrades of computing nodes for optimizing utility effectiveness in cloud computing services. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2011 holdings: @attributes: islocal: N |
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