FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFC |
Texto Completo: | http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13197 |
Resumo: | Currently, many customers and providers are using resources of Cloud Computing environments,such as processing and storage, for their applications and services. Through ease of use, based on the pay per use model, it is natural that the number of users and their workloads also grow. As a result, providers should expand their resources and maintain the agreed level of quality for customers, otherwise breaks the Service Level Agreement (SLA) and the resulting penalties. With the increase in computational resources usage, a key feature of Cloud Computing has become quite attractive: the elasticity. Elasticity can be defined as how a computational cloud adapts to variations in its workload through resources provisioning and deprovisioning. Due to limited availability of information regarding configuration of the experiments, in general is not trivial to implement elasticity concepts, much less apply them in cloud environments. Furthermore, the way of measuring cloud elasticity is not obvious, and there is not yet a standard for this task. Moreover, its evaluation could be performed in different ways due to many technologies and strategies for providing cloud elasticity. A common aspect of elasticity performance analysis is the use of environmental resources, such as CPU and memory, and even without a specific metric, to allow an indirectly assess of elasticity. In this context, this work proposes FOLE, a conceptual framework for conducting performance analysis of elasticity in Cloud Computing environments in a systematic, flexible and reproducible way. To support the framework, we proposed a set of specific metrics for elasticity and metrics for its indirect measurement. For the measurement of elasticity in Cloud Computing, we proposed metrics based on concepts of Physics, such as strain and stress, and Microeconomics, such as Price Elasticity of Demand. Additionally, we also proposed metrics based on resources allocation and deallocation operation times, and used resources, to support the measurement of elasticity. For verification and validation of the proposal, we performed two experiments, one in a private cloud and other in a hybrid cloud, using microbenchmarks and a classic scientific application, through a designed infrastructure based on concepts of Autonomic Computing. Through these experiments, FOLE had validated their activities, allowing the systematization of a elasticity performance analysis. The results show it is possible to assess the elasticity of a Cloud Computing environment using specific metrics based on other areas of knowledge, and also complemented by metrics related to time and resources operations satisfactorily. |
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Biblioteca Digital de Teses e Dissertações da UFC |
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info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisFOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments FOLE: Um Framework Conceitual para AvaliaÃÃo de Desempenho da Elasticidade em Ambientes de ComputaÃÃo em Nuvem2014-11-03Josà Neuman de Souza09779604391http://lattes.cnpq.br/3614256141054800Danielo GonÃalves Gomes42593751304//lattes.cnpq.br/6303297687237256Fernando Antonio Mota Trinta49395653353http://lattes.cnpq.br/8908026219336623Bruno Richard Schulze54397529787http://lattes.cnpq.br/4448540530244733StÃnio FlÃvio de Lacerda Fernandes53393406434http://lattes.cnpq.br/8598484164048317Gabriel Antoine Louis Paillard54695767368 http://lattes.cnpq.br/442757826430341654909546391 http://lattes.cnpq.br/9359546788802277Emanuel Ferreira CoutinhoUniversidade Federal do CearÃPrograma de PÃs-GraduaÃÃo em CiÃncia da ComputaÃÃoUFCBRComputaÃÃo em NuvemElasticidade AnÃlise de Desempenho MetodologiaCloud Computing. Elasticity. Performance Analysis. Methodology.CIENCIA DA COMPUTACAOCurrently, many customers and providers are using resources of Cloud Computing environments,such as processing and storage, for their applications and services. Through ease of use, based on the pay per use model, it is natural that the number of users and their workloads also grow. As a result, providers should expand their resources and maintain the agreed level of quality for customers, otherwise breaks the Service Level Agreement (SLA) and the resulting penalties. With the increase in computational resources usage, a key feature of Cloud Computing has become quite attractive: the elasticity. Elasticity can be defined as how a computational cloud adapts to variations in its workload through resources provisioning and deprovisioning. Due to limited availability of information regarding configuration of the experiments, in general is not trivial to implement elasticity concepts, much less apply them in cloud environments. Furthermore, the way of measuring cloud elasticity is not obvious, and there is not yet a standard for this task. Moreover, its evaluation could be performed in different ways due to many technologies and strategies for providing cloud elasticity. A common aspect of elasticity performance analysis is the use of environmental resources, such as CPU and memory, and even without a specific metric, to allow an indirectly assess of elasticity. In this context, this work proposes FOLE, a conceptual framework for conducting performance analysis of elasticity in Cloud Computing environments in a systematic, flexible and reproducible way. To support the framework, we proposed a set of specific metrics for elasticity and metrics for its indirect measurement. For the measurement of elasticity in Cloud Computing, we proposed metrics based on concepts of Physics, such as strain and stress, and Microeconomics, such as Price Elasticity of Demand. Additionally, we also proposed metrics based on resources allocation and deallocation operation times, and used resources, to support the measurement of elasticity. For verification and validation of the proposal, we performed two experiments, one in a private cloud and other in a hybrid cloud, using microbenchmarks and a classic scientific application, through a designed infrastructure based on concepts of Autonomic Computing. Through these experiments, FOLE had validated their activities, allowing the systematization of a elasticity performance analysis. The results show it is possible to assess the elasticity of a Cloud Computing environment using specific metrics based on other areas of knowledge, and also complemented by metrics related to time and resources operations satisfactorily.Atualmente muitos clientes e provedores estÃo utilizando recursos de ambientes de ComputaÃÃo em Nuvem, tais como processamento e armazenamento, para suas aplicaÃÃes e serviÃos. Devido à facilidade de utilizaÃÃo, baseada no modelo de pagamento por uso, à natural que a quantidade de usuÃrios e suas respectivas cargas de trabalho tambÃm cresÃam. Como consequÃncia, os provedores devem ampliar seus recursos e manter o nÃvel de qualidade acordado com os clientes, sob pena de quebras do Service Level Agreement (SLA) e consequentes multas. Com o aumento na utilizaÃÃo de recursos computacionais, uma das caracterÃsticas principais da ComputaÃÃo em Nuvem tem se tornado bastante atrativa: a elasticidade. A elasticidade pode ser definida como o quanto uma nuvem computacional se adapta a variaÃÃes na sua carga de trabalho atravÃs do provisionamento e desprovisionamento de recursos. Devido à pouca disponibilidade de informaÃÃo em relaÃÃo à configuraÃÃo dos experimentos, em geral nÃo à trivial implementar conceitos de elasticidade, muito menos aplicÃ-los em ambientes de nuvens computacionais. AlÃm disso, a maneira de se medir a elasticidade nÃo à tÃo Ãbvia, e bastante variada, nÃo havendo ainda uma padronizaÃÃo para esta tarefa, e sua avaliaÃÃo pode ser executada de diferentes maneiras devido Ãs diversas tecnologias e estratÃgias para o provimento da elasticidade. Um aspecto comum na avaliaÃÃo de desempenho da elasticidade à a utilizaÃÃo de recursos do ambiente, como CPU e memÃria, e mesmo sem ter uma mÃtrica especÃfica para a elasticidade, à possÃvel se obter uma avaliaÃÃo indireta. Nesse contexto, este trabalho propÃe o FOLE, um framework conceitual para a realizaÃÃo de anÃlise de desempenho da elasticidade em nuvens computacionais de maneira sistemÃtica, flexÃvel e reproduzÃvel. Para apoiar o framework, mÃtricas especÃficas para a elasticidade e mÃtricas para sua mediÃÃo indireta foram propostas. Para a mediÃÃo da elasticidade em ComputaÃÃo em Nuvem, propomos mÃtricas baseadas em conceitos da FÃsica, como tensÃo e estresse, e da Microeconomia, como Elasticidade do PreÃo da Demanda. Adicionalmente, mÃtricas baseadas em tempos de operaÃÃes de alocaÃÃo e desalocaÃÃo de recursos, e na utilizaÃÃo desses recursos foram propostas para apoiar a mediÃÃo da elasticidade. Para verificaÃÃo e validaÃÃo da proposta, dois estudos de caso foram realizados, um em uma nuvem privada e outro em uma nuvem hÃbrida, com experimentos projetados utilizando microbenchmarks e uma aplicaÃÃo cientÃfica clÃssica, executados sobre uma infraestrutura baseada em conceitos de ComputaÃÃo AutonÃmica. Por meio desses experimentos, o FOLE foi validado em suas atividades, permitindo a sistematizaÃÃo de uma anÃlise de desempenho da elasticidade. Os resultados mostram que à possÃvel avaliar a elasticidade de um ambiente de ComputaÃÃo em Nuvem por meio de mÃtricas especÃficas baseadas em conceitos de outras Ãreas de conhecimento, e tambÃm complementada por mÃtricas relacionadas a tempos de operaÃÃes e recursos de maneira satisfatÃria. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13197application/pdfinfo:eu-repo/semantics/openAccessporreponame:Biblioteca Digital de Teses e Dissertações da UFCinstname:Universidade Federal do Cearáinstacron:UFC2019-01-21T11:26:28Zmail@mail.com - |
dc.title.en.fl_str_mv |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments |
dc.title.alternative.pt.fl_str_mv |
FOLE: Um Framework Conceitual para AvaliaÃÃo de Desempenho da Elasticidade em Ambientes de ComputaÃÃo em Nuvem |
title |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments |
spellingShingle |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments Emanuel Ferreira Coutinho ComputaÃÃo em Nuvem Elasticidade AnÃlise de Desempenho Metodologia Cloud Computing. Elasticity. Performance Analysis. Methodology. CIENCIA DA COMPUTACAO |
title_short |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments |
title_full |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments |
title_fullStr |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments |
title_full_unstemmed |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments |
title_sort |
FOLE: A Conceptual Framework for Elasticity Performance Analysis in Cloud Computing Environments |
author |
Emanuel Ferreira Coutinho |
author_facet |
Emanuel Ferreira Coutinho |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Josà Neuman de Souza |
dc.contributor.advisor1ID.fl_str_mv |
09779604391 |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/3614256141054800 |
dc.contributor.advisor-co1.fl_str_mv |
Danielo GonÃalves Gomes |
dc.contributor.advisor-co1ID.fl_str_mv |
42593751304 |
dc.contributor.advisor-co1Lattes.fl_str_mv |
//lattes.cnpq.br/6303297687237256 |
dc.contributor.referee1.fl_str_mv |
Fernando Antonio Mota Trinta |
dc.contributor.referee1ID.fl_str_mv |
49395653353 |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/8908026219336623 |
dc.contributor.referee2.fl_str_mv |
Bruno Richard Schulze |
dc.contributor.referee2ID.fl_str_mv |
54397529787 |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/4448540530244733 |
dc.contributor.referee3.fl_str_mv |
StÃnio FlÃvio de Lacerda Fernandes |
dc.contributor.referee3ID.fl_str_mv |
53393406434 |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/8598484164048317 |
dc.contributor.referee4.fl_str_mv |
Gabriel Antoine Louis Paillard |
dc.contributor.referee4ID.fl_str_mv |
54695767368 |
dc.contributor.referee4Lattes.fl_str_mv |
http://lattes.cnpq.br/4427578264303416 |
dc.contributor.authorID.fl_str_mv |
54909546391 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/9359546788802277 |
dc.contributor.author.fl_str_mv |
Emanuel Ferreira Coutinho |
contributor_str_mv |
Josà Neuman de Souza Danielo GonÃalves Gomes Fernando Antonio Mota Trinta Bruno Richard Schulze StÃnio FlÃvio de Lacerda Fernandes Gabriel Antoine Louis Paillard |
dc.subject.por.fl_str_mv |
ComputaÃÃo em Nuvem Elasticidade AnÃlise de Desempenho Metodologia |
topic |
ComputaÃÃo em Nuvem Elasticidade AnÃlise de Desempenho Metodologia Cloud Computing. Elasticity. Performance Analysis. Methodology. CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Cloud Computing. Elasticity. Performance Analysis. Methodology. |
dc.subject.cnpq.fl_str_mv |
CIENCIA DA COMPUTACAO |
dc.description.abstract.por.fl_txt_mv |
Currently, many customers and providers are using resources of Cloud Computing environments,such as processing and storage, for their applications and services. Through ease of use, based on the pay per use model, it is natural that the number of users and their workloads also grow. As a result, providers should expand their resources and maintain the agreed level of quality for customers, otherwise breaks the Service Level Agreement (SLA) and the resulting penalties. With the increase in computational resources usage, a key feature of Cloud Computing has become quite attractive: the elasticity. Elasticity can be defined as how a computational cloud adapts to variations in its workload through resources provisioning and deprovisioning. Due to limited availability of information regarding configuration of the experiments, in general is not trivial to implement elasticity concepts, much less apply them in cloud environments. Furthermore, the way of measuring cloud elasticity is not obvious, and there is not yet a standard for this task. Moreover, its evaluation could be performed in different ways due to many technologies and strategies for providing cloud elasticity. A common aspect of elasticity performance analysis is the use of environmental resources, such as CPU and memory, and even without a specific metric, to allow an indirectly assess of elasticity. In this context, this work proposes FOLE, a conceptual framework for conducting performance analysis of elasticity in Cloud Computing environments in a systematic, flexible and reproducible way. To support the framework, we proposed a set of specific metrics for elasticity and metrics for its indirect measurement. For the measurement of elasticity in Cloud Computing, we proposed metrics based on concepts of Physics, such as strain and stress, and Microeconomics, such as Price Elasticity of Demand. Additionally, we also proposed metrics based on resources allocation and deallocation operation times, and used resources, to support the measurement of elasticity. For verification and validation of the proposal, we performed two experiments, one in a private cloud and other in a hybrid cloud, using microbenchmarks and a classic scientific application, through a designed infrastructure based on concepts of Autonomic Computing. Through these experiments, FOLE had validated their activities, allowing the systematization of a elasticity performance analysis. The results show it is possible to assess the elasticity of a Cloud Computing environment using specific metrics based on other areas of knowledge, and also complemented by metrics related to time and resources operations satisfactorily. Atualmente muitos clientes e provedores estÃo utilizando recursos de ambientes de ComputaÃÃo em Nuvem, tais como processamento e armazenamento, para suas aplicaÃÃes e serviÃos. Devido à facilidade de utilizaÃÃo, baseada no modelo de pagamento por uso, à natural que a quantidade de usuÃrios e suas respectivas cargas de trabalho tambÃm cresÃam. Como consequÃncia, os provedores devem ampliar seus recursos e manter o nÃvel de qualidade acordado com os clientes, sob pena de quebras do Service Level Agreement (SLA) e consequentes multas. Com o aumento na utilizaÃÃo de recursos computacionais, uma das caracterÃsticas principais da ComputaÃÃo em Nuvem tem se tornado bastante atrativa: a elasticidade. A elasticidade pode ser definida como o quanto uma nuvem computacional se adapta a variaÃÃes na sua carga de trabalho atravÃs do provisionamento e desprovisionamento de recursos. Devido à pouca disponibilidade de informaÃÃo em relaÃÃo à configuraÃÃo dos experimentos, em geral nÃo à trivial implementar conceitos de elasticidade, muito menos aplicÃ-los em ambientes de nuvens computacionais. AlÃm disso, a maneira de se medir a elasticidade nÃo à tÃo Ãbvia, e bastante variada, nÃo havendo ainda uma padronizaÃÃo para esta tarefa, e sua avaliaÃÃo pode ser executada de diferentes maneiras devido Ãs diversas tecnologias e estratÃgias para o provimento da elasticidade. Um aspecto comum na avaliaÃÃo de desempenho da elasticidade à a utilizaÃÃo de recursos do ambiente, como CPU e memÃria, e mesmo sem ter uma mÃtrica especÃfica para a elasticidade, à possÃvel se obter uma avaliaÃÃo indireta. Nesse contexto, este trabalho propÃe o FOLE, um framework conceitual para a realizaÃÃo de anÃlise de desempenho da elasticidade em nuvens computacionais de maneira sistemÃtica, flexÃvel e reproduzÃvel. Para apoiar o framework, mÃtricas especÃficas para a elasticidade e mÃtricas para sua mediÃÃo indireta foram propostas. Para a mediÃÃo da elasticidade em ComputaÃÃo em Nuvem, propomos mÃtricas baseadas em conceitos da FÃsica, como tensÃo e estresse, e da Microeconomia, como Elasticidade do PreÃo da Demanda. Adicionalmente, mÃtricas baseadas em tempos de operaÃÃes de alocaÃÃo e desalocaÃÃo de recursos, e na utilizaÃÃo desses recursos foram propostas para apoiar a mediÃÃo da elasticidade. Para verificaÃÃo e validaÃÃo da proposta, dois estudos de caso foram realizados, um em uma nuvem privada e outro em uma nuvem hÃbrida, com experimentos projetados utilizando microbenchmarks e uma aplicaÃÃo cientÃfica clÃssica, executados sobre uma infraestrutura baseada em conceitos de ComputaÃÃo AutonÃmica. Por meio desses experimentos, o FOLE foi validado em suas atividades, permitindo a sistematizaÃÃo de uma anÃlise de desempenho da elasticidade. Os resultados mostram que à possÃvel avaliar a elasticidade de um ambiente de ComputaÃÃo em Nuvem por meio de mÃtricas especÃficas baseadas em conceitos de outras Ãreas de conhecimento, e tambÃm complementada por mÃtricas relacionadas a tempos de operaÃÃes e recursos de maneira satisfatÃria. |
description |
Currently, many customers and providers are using resources of Cloud Computing environments,such as processing and storage, for their applications and services. Through ease of use, based on the pay per use model, it is natural that the number of users and their workloads also grow. As a result, providers should expand their resources and maintain the agreed level of quality for customers, otherwise breaks the Service Level Agreement (SLA) and the resulting penalties. With the increase in computational resources usage, a key feature of Cloud Computing has become quite attractive: the elasticity. Elasticity can be defined as how a computational cloud adapts to variations in its workload through resources provisioning and deprovisioning. Due to limited availability of information regarding configuration of the experiments, in general is not trivial to implement elasticity concepts, much less apply them in cloud environments. Furthermore, the way of measuring cloud elasticity is not obvious, and there is not yet a standard for this task. Moreover, its evaluation could be performed in different ways due to many technologies and strategies for providing cloud elasticity. A common aspect of elasticity performance analysis is the use of environmental resources, such as CPU and memory, and even without a specific metric, to allow an indirectly assess of elasticity. In this context, this work proposes FOLE, a conceptual framework for conducting performance analysis of elasticity in Cloud Computing environments in a systematic, flexible and reproducible way. To support the framework, we proposed a set of specific metrics for elasticity and metrics for its indirect measurement. For the measurement of elasticity in Cloud Computing, we proposed metrics based on concepts of Physics, such as strain and stress, and Microeconomics, such as Price Elasticity of Demand. Additionally, we also proposed metrics based on resources allocation and deallocation operation times, and used resources, to support the measurement of elasticity. For verification and validation of the proposal, we performed two experiments, one in a private cloud and other in a hybrid cloud, using microbenchmarks and a classic scientific application, through a designed infrastructure based on concepts of Autonomic Computing. Through these experiments, FOLE had validated their activities, allowing the systematization of a elasticity performance analysis. The results show it is possible to assess the elasticity of a Cloud Computing environment using specific metrics based on other areas of knowledge, and also complemented by metrics related to time and resources operations satisfactorily. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-11-03 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
status_str |
publishedVersion |
format |
doctoralThesis |
dc.identifier.uri.fl_str_mv |
http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13197 |
url |
http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=13197 |
dc.language.iso.fl_str_mv |
por |
language |
por |
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info:eu-repo/semantics/openAccess |
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openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal do Cearà |
dc.publisher.program.fl_str_mv |
Programa de PÃs-GraduaÃÃo em CiÃncia da ComputaÃÃo |
dc.publisher.initials.fl_str_mv |
UFC |
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BR |
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Universidade Federal do Cearà |
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reponame:Biblioteca Digital de Teses e Dissertações da UFC instname:Universidade Federal do Ceará instacron:UFC |
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Universidade Federal do Ceará |
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UFC |
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mail@mail.com |
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