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Performance Evaluation of Data-Intensive Computing Applications on a Public IaaS Cloud
dc.contributor.author | Expósito, Roberto R. | |
dc.contributor.author | Taboada, Guillermo L. | |
dc.contributor.author | Ramos Garea, Sabela | |
dc.contributor.author | Touriño, Juan | |
dc.contributor.author | Doallo, Ramón | |
dc.date.accessioned | 2018-07-04T17:26:45Z | |
dc.date.available | 2018-07-04T17:26:45Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Expósito, R. R., Taboada, G. L., Ramos, S., Touriño, J., & Doallo, R. (2016). Performance evaluation of data-intensive computing applications on a public IaaS cloud. The Computer Journal, 59(3), 287-307. | es_ES |
dc.identifier.issn | 0010-4620 | |
dc.identifier.issn | 1460-2067 | |
dc.identifier.uri | http://hdl.handle.net/2183/20849 | |
dc.description | This is a post-peer-review, pre-copyedit version of an article published in The Computer Journal. The final authenticated version is available online at: https://doi.org/10.1093/comjnl/bxu111 | |
dc.description.abstract | [Abstract] The advent of cloud computing technologies, which dynamically provide on-demand access to computational resources over the Internet, is offering new possibilities to many scientists and researchers. Nowadays, Infrastructure as a Service (IaaS) cloud providers can offset the increasing processing requirements of data-intensive computing applications, becoming an emerging alternative to traditional servers and clusters. In this paper, a comprehensive study of the leading public IaaS cloud platform, Amazon EC2, has been conducted in order to assess its suitability for data-intensive computing. One of the key contributions of this work is the analysis of the storage-optimized family of EC2 instances. Furthermore, this study presents a detailed analysis of both performance and cost metrics. More specifically, multiple experiments have been carried out to analyze the full I/O software stack, ranging from the low-level storage devices and cluster file systems up to real-world applications using representative data-intensive parallel codes and MapReduce-based workloads. The analysis of the experimental results has shown that data-intensive applications can benefit from tailored EC2-based virtual clusters, enabling users to obtain the highest performance and cost-effectiveness in the cloud. | es_ES |
dc.description.sponsorship | Ministerio de Economía y Competitividad; TIN2013-42148-P | es_ES |
dc.description.sponsorship | Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/055 | es_ES |
dc.description.sponsorship | Ministerio de Educación y Ciencia; AP2010-4348 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Oxford University Press | es_ES |
dc.relation.uri | https://doi.org/10.1093/comjnl/bxu111 | es_ES |
dc.subject | Data intensive computing | es_ES |
dc.subject | Cloud computing | es_ES |
dc.subject | Infrastructure as a service | es_ES |
dc.subject | Amazon EC2 | es_ES |
dc.subject | Cluster file system | es_ES |
dc.subject | MapReduce | es_ES |
dc.title | Performance Evaluation of Data-Intensive Computing Applications on a Public IaaS Cloud | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | The Computer Journal | es_ES |
UDC.volume | 59 | es_ES |
UDC.issue | 3 | es_ES |
UDC.startPage | 287 | es_ES |
UDC.endPage | 307 | es_ES |
dc.identifier.doi | 10.1093/comjnl/bxu111 |
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