Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers
Ano de defesa: | 2018 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal Rural de Pernambuco
|
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Informática Aplicada
|
Departamento: |
Departamento de Estatística e Informática
|
País: |
Brasil
|
Palavras-chave em Português: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7858 |
Resumo: | A growing concern in society is related about sustainability and the environmental impacts caused by energy consumption and generation. In deed, there is great global pressure for companies to adopt sustainable practices, not only because of the nancial savings from reducing energy consumption, but also due to the awareness of the depletion of natural resources for future generations. Considering the advancements of technology, the society needs more interconnected services to the Internet and, consequently, the infrastructure demanded to support for these services also contributes with a signi cant impact on the consumption of electricity. In recent years, due to factors such as social networking, cloud computing, and ecommerce, data center has been growing in importance and, so, its reliability, collaboration, availability, and cost reduction represent elements under studies. The data center systems require an entire infrastructure with redundancy mechanisms to operate with high availability, a fact that may have a negative impact on both the electrical energy consumption and operational cost. This work proposes a method based on multiobjective genetic algorithms to optimize cost, environmental impact and availability of electrical energy infrastructures of data centers. The main goal is to maximize the availability, minimize the total cost and the operational exergy (used to estimate the environmental impact). To compute such metrics, models were proposed using the Energy Flow Model (EFM), Reliability Block Diagram (RBD) and Stochastic Petri Net (SPN). Two case studies are conducted to show the applicability of the proposed strategy: (i) takes into account 5 typical data center architectures that were optimized to conduct the validation process of the proposed strategy; (ii) uses the optimization strategy in four architectures classi ed by ANSI / TIA-942 (TIER I to TIER IV). In both case studies, signi cant improvements were achieved in the results, which were very close to the optimum one that was obtained by a brute force algorithm that analyzes all the possibilities and returns the optimal solution. It is worth mentioning that the time used to obtain the results using the genetic algorithm approach was signi cantly lower (6,763,260 times), in comparison with the strategy which combines all the possible combinations to obtain the optimal result. |
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CALLOU, Gustavo Rau de AlmeidaCALLOU, Gustavo Rau de AlmeidaSOUSA, Erica Teixeira Gomes deGALDINO, Sérgio Mário Linshttp://lattes.cnpq.br/3489418183765779AUSTRÉGESILO, Márcio Sérgio Soares2019-02-20T13:52:47Z2018-04-19AUSTRÉGESILO, Márcio Sérgio Soares. Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers. 2018. 116 f. Dissertação (Programa de Pós-Graduação em Informática Aplicada) - Universidade Federal Rural de Pernambuco, Recife.http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7858A growing concern in society is related about sustainability and the environmental impacts caused by energy consumption and generation. In deed, there is great global pressure for companies to adopt sustainable practices, not only because of the nancial savings from reducing energy consumption, but also due to the awareness of the depletion of natural resources for future generations. Considering the advancements of technology, the society needs more interconnected services to the Internet and, consequently, the infrastructure demanded to support for these services also contributes with a signi cant impact on the consumption of electricity. In recent years, due to factors such as social networking, cloud computing, and ecommerce, data center has been growing in importance and, so, its reliability, collaboration, availability, and cost reduction represent elements under studies. The data center systems require an entire infrastructure with redundancy mechanisms to operate with high availability, a fact that may have a negative impact on both the electrical energy consumption and operational cost. This work proposes a method based on multiobjective genetic algorithms to optimize cost, environmental impact and availability of electrical energy infrastructures of data centers. The main goal is to maximize the availability, minimize the total cost and the operational exergy (used to estimate the environmental impact). To compute such metrics, models were proposed using the Energy Flow Model (EFM), Reliability Block Diagram (RBD) and Stochastic Petri Net (SPN). Two case studies are conducted to show the applicability of the proposed strategy: (i) takes into account 5 typical data center architectures that were optimized to conduct the validation process of the proposed strategy; (ii) uses the optimization strategy in four architectures classi ed by ANSI / TIA-942 (TIER I to TIER IV). In both case studies, signi cant improvements were achieved in the results, which were very close to the optimum one that was obtained by a brute force algorithm that analyzes all the possibilities and returns the optimal solution. It is worth mentioning that the time used to obtain the results using the genetic algorithm approach was signi cantly lower (6,763,260 times), in comparison with the strategy which combines all the possible combinations to obtain the optimal result.É crescente, na sociedade, a preocupação com a sustentabilidade e os impactos ambientais causados pelo consumo e geração de energia. Existe uma grande pressão mundial para que empresas adotem práticas sustentáveis, não só pela economia financeira decorrente da redução do consumo de energia, como pela conscientização do esgotamento dos recursos naturais para gerações futuras. Considerando o avanço da tecnologia, a sociedade vem necessitando de mais serviços interligados à Internet e, consequentemente, de infraestrutura para esses serviços, fator que impacta de maneira signi cativa no consumo de energia elétrica. Nos últimos anos, o crescimento da tecnologia vem demandando uma maior con abilidade, acessibilidade, colaboração, disponibilidade e redução de custos dos data centers, devido a fatores como redes sociais, computação nas nuvens e comércio eletrônico. Esses sistemas necessitam de toda uma infraestrutura com mecanismos de redundância para funcionar com alta disponibilidade, fato que implica num grande consumo de energia elétrica impactando na sustentabilidade e custo operacional. Este trabalho propõe a utilização de algoritmos genéticos multiobjetivos para otimizar custo, impacto ambiental e disponibilidade da infraestrutura de energia elétrica desses sistemas. O objetivo é maximizar a disponibilidade e minimizar o custo total e a exergia operacional (utilizada para estimar o impacto ambiental). Para se computar tais métricas são utilizados o Modelo de Fluxo de Energia (EFM), o Diagrama de Bloco de Con abilidade (RBD) e a Rede de Petri Estocástica (SPN). Dois estudos de caso são conduzidos: (i) leva em consideração 5 arquiteturas tópicas de data centers para mostrar a aplicabilidade e validação da estratégia proposta; (ii) utiliza a estratégia de otimização em quatro arquiteturas classi cadas pela norma ANSI/TIA-942 (TIER I à TIER IV). Em ambos estudos de caso, observou-se uma melhora signi cativa nos resultados que caram bem próximos ao ótimo que foi obtido por um algoritmo de forçaa bruta que analisa todas as possibilidades e retorna a solução ótima. Vale ressaltar que o tempo utilizado para se obter as respostas utilizando a abordagem com algoritmo genético foi signi cativamente inferior (6.763.260 vezes) se comparado a uma estratégia que combina todas as possibilidades para obter o resultado ótimo.Submitted by Mario BC (mario@bc.ufrpe.br) on 2019-02-20T13:52:47Z No. of bitstreams: 1 Marcio Sergio Soares Austregesilo.pdf: 1826674 bytes, checksum: 774c5301b3533f73f8f082823ff96c59 (MD5)Made available in DSpace on 2019-02-20T13:52:47Z (GMT). No. of bitstreams: 1 Marcio Sergio Soares Austregesilo.pdf: 1826674 bytes, checksum: 774c5301b3533f73f8f082823ff96c59 (MD5) Previous issue date: 2018-04-19application/pdfporUniversidade Federal Rural de PernambucoPrograma de Pós-Graduação em Informática AplicadaUFRPEBrasilDepartamento de Estatística e InformáticaAlgoritmo genéticoSustentabilidadeEnergia elétricaData centerCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOUma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centersinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis-8268485641417162699600600600-67745551403961205013671711205811204509info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRPEinstname:Universidade Federal Rural de Pernambuco (UFRPE)instacron:UFRPEORIGINALMarcio Sergio Soares Austregesilo.pdfMarcio Sergio Soares Austregesilo.pdfapplication/pdf1826674http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7858/2/Marcio+Sergio+Soares+Austregesilo.pdf774c5301b3533f73f8f082823ff96c59MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7858/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede2/78582019-02-20 10:52:47.95oai:tede2:tede2/7858Tk9UQTogQ09MT1FVRSBBUVVJIEEgU1VBIFBSw5NQUklBIExJQ0VOw4dBCkVzdGEgbGljZW7Dp2EgZGUgZXhlbXBsbyDDqSBmb3JuZWNpZGEgYXBlbmFzIHBhcmEgZmlucyBpbmZvcm1hdGl2b3MuCgpMSUNFTsOHQSBERSBESVNUUklCVUnDh8ODTyBOw4NPLUVYQ0xVU0lWQQoKQ29tIGEgYXByZXNlbnRhw6fDo28gZGVzdGEgbGljZW7Dp2EsIHZvY8OqIChvIGF1dG9yIChlcykgb3UgbyB0aXR1bGFyIGRvcyBkaXJlaXRvcyBkZSBhdXRvcikgY29uY2VkZSDDoCBVbml2ZXJzaWRhZGUgClhYWCAoU2lnbGEgZGEgVW5pdmVyc2lkYWRlKSBvIGRpcmVpdG8gbsOjby1leGNsdXNpdm8gZGUgcmVwcm9kdXppciwgIHRyYWR1emlyIChjb25mb3JtZSBkZWZpbmlkbyBhYmFpeG8pLCBlL291IApkaXN0cmlidWlyIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyAoaW5jbHVpbmRvIG8gcmVzdW1vKSBwb3IgdG9kbyBvIG11bmRvIG5vIGZvcm1hdG8gaW1wcmVzc28gZSBlbGV0csO0bmljbyBlIAplbSBxdWFscXVlciBtZWlvLCBpbmNsdWluZG8gb3MgZm9ybWF0b3Mgw6F1ZGlvIG91IHbDrWRlby4KClZvY8OqIGNvbmNvcmRhIHF1ZSBhIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSBwb2RlLCBzZW0gYWx0ZXJhciBvIGNvbnRlw7pkbywgdHJhbnNwb3IgYSBzdWEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvIApwYXJhIHF1YWxxdWVyIG1laW8gb3UgZm9ybWF0byBwYXJhIGZpbnMgZGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIHRhbWLDqW0gY29uY29yZGEgcXVlIGEgU2lnbGEgZGUgVW5pdmVyc2lkYWRlIHBvZGUgbWFudGVyIG1haXMgZGUgdW1hIGPDs3BpYSBhIHN1YSB0ZXNlIG91IApkaXNzZXJ0YcOnw6NvIHBhcmEgZmlucyBkZSBzZWd1cmFuw6dhLCBiYWNrLXVwIGUgcHJlc2VydmHDp8Ojby4KClZvY8OqIGRlY2xhcmEgcXVlIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyDDqSBvcmlnaW5hbCBlIHF1ZSB2b2PDqiB0ZW0gbyBwb2RlciBkZSBjb25jZWRlciBvcyBkaXJlaXRvcyBjb250aWRvcyAKbmVzdGEgbGljZW7Dp2EuIFZvY8OqIHRhbWLDqW0gZGVjbGFyYSBxdWUgbyBkZXDDs3NpdG8gZGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyBuw6NvLCBxdWUgc2VqYSBkZSBzZXUgCmNvbmhlY2ltZW50bywgaW5mcmluZ2UgZGlyZWl0b3MgYXV0b3JhaXMgZGUgbmluZ3XDqW0uCgpDYXNvIGEgc3VhIHRlc2Ugb3UgZGlzc2VydGHDp8OjbyBjb250ZW5oYSBtYXRlcmlhbCBxdWUgdm9jw6ogbsOjbyBwb3NzdWkgYSB0aXR1bGFyaWRhZGUgZG9zIGRpcmVpdG9zIGF1dG9yYWlzLCB2b2PDqiAKZGVjbGFyYSBxdWUgb2J0ZXZlIGEgcGVybWlzc8OjbyBpcnJlc3RyaXRhIGRvIGRldGVudG9yIGRvcyBkaXJlaXRvcyBhdXRvcmFpcyBwYXJhIGNvbmNlZGVyIMOgIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSAKb3MgZGlyZWl0b3MgYXByZXNlbnRhZG9zIG5lc3RhIGxpY2Vuw6dhLCBlIHF1ZSBlc3NlIG1hdGVyaWFsIGRlIHByb3ByaWVkYWRlIGRlIHRlcmNlaXJvcyBlc3TDoSBjbGFyYW1lbnRlIAppZGVudGlmaWNhZG8gZSByZWNvbmhlY2lkbyBubyB0ZXh0byBvdSBubyBjb250ZcO6ZG8gZGEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvIG9yYSBkZXBvc2l0YWRhLgoKQ0FTTyBBIFRFU0UgT1UgRElTU0VSVEHDh8ODTyBPUkEgREVQT1NJVEFEQSBURU5IQSBTSURPIFJFU1VMVEFETyBERSBVTSBQQVRST0PDjU5JTyBPVSAKQVBPSU8gREUgVU1BIEFHw4pOQ0lBIERFIEZPTUVOVE8gT1UgT1VUUk8gT1JHQU5JU01PIFFVRSBOw4NPIFNFSkEgQSBTSUdMQSBERSAKVU5JVkVSU0lEQURFLCBWT0PDiiBERUNMQVJBIFFVRSBSRVNQRUlUT1UgVE9ET1MgRSBRVUFJU1FVRVIgRElSRUlUT1MgREUgUkVWSVPDg08gQ09NTyAKVEFNQsOJTSBBUyBERU1BSVMgT0JSSUdBw4fDlUVTIEVYSUdJREFTIFBPUiBDT05UUkFUTyBPVSBBQ09SRE8uCgpBIFNpZ2xhIGRlIFVuaXZlcnNpZGFkZSBzZSBjb21wcm9tZXRlIGEgaWRlbnRpZmljYXIgY2xhcmFtZW50ZSBvIHNldSBub21lIChzKSBvdSBvKHMpIG5vbWUocykgZG8ocykgCmRldGVudG9yKGVzKSBkb3MgZGlyZWl0b3MgYXV0b3JhaXMgZGEgdGVzZSBvdSBkaXNzZXJ0YcOnw6NvLCBlIG7Do28gZmFyw6EgcXVhbHF1ZXIgYWx0ZXJhw6fDo28sIGFsw6ltIGRhcXVlbGFzIApjb25jZWRpZGFzIHBvciBlc3RhIGxpY2Vuw6dhLgo=Biblioteca Digital de Teses e Dissertaçõeshttp://www.tede2.ufrpe.br:8080/tede/PUBhttp://www.tede2.ufrpe.br:8080/oai/requestbdtd@ufrpe.br ||bdtd@ufrpe.bropendoar:2019-02-20T13:52:47Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE)false |
dc.title.por.fl_str_mv |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers |
title |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers |
spellingShingle |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers AUSTRÉGESILO, Márcio Sérgio Soares Algoritmo genético Sustentabilidade Energia elétrica Data center CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers |
title_full |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers |
title_fullStr |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers |
title_full_unstemmed |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers |
title_sort |
Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers |
author |
AUSTRÉGESILO, Márcio Sérgio Soares |
author_facet |
AUSTRÉGESILO, Márcio Sérgio Soares |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
CALLOU, Gustavo Rau de Almeida |
dc.contributor.referee1.fl_str_mv |
CALLOU, Gustavo Rau de Almeida |
dc.contributor.referee2.fl_str_mv |
SOUSA, Erica Teixeira Gomes de |
dc.contributor.referee3.fl_str_mv |
GALDINO, Sérgio Mário Lins |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/3489418183765779 |
dc.contributor.author.fl_str_mv |
AUSTRÉGESILO, Márcio Sérgio Soares |
contributor_str_mv |
CALLOU, Gustavo Rau de Almeida CALLOU, Gustavo Rau de Almeida SOUSA, Erica Teixeira Gomes de GALDINO, Sérgio Mário Lins |
dc.subject.por.fl_str_mv |
Algoritmo genético Sustentabilidade Energia elétrica Data center |
topic |
Algoritmo genético Sustentabilidade Energia elétrica Data center CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
A growing concern in society is related about sustainability and the environmental impacts caused by energy consumption and generation. In deed, there is great global pressure for companies to adopt sustainable practices, not only because of the nancial savings from reducing energy consumption, but also due to the awareness of the depletion of natural resources for future generations. Considering the advancements of technology, the society needs more interconnected services to the Internet and, consequently, the infrastructure demanded to support for these services also contributes with a signi cant impact on the consumption of electricity. In recent years, due to factors such as social networking, cloud computing, and ecommerce, data center has been growing in importance and, so, its reliability, collaboration, availability, and cost reduction represent elements under studies. The data center systems require an entire infrastructure with redundancy mechanisms to operate with high availability, a fact that may have a negative impact on both the electrical energy consumption and operational cost. This work proposes a method based on multiobjective genetic algorithms to optimize cost, environmental impact and availability of electrical energy infrastructures of data centers. The main goal is to maximize the availability, minimize the total cost and the operational exergy (used to estimate the environmental impact). To compute such metrics, models were proposed using the Energy Flow Model (EFM), Reliability Block Diagram (RBD) and Stochastic Petri Net (SPN). Two case studies are conducted to show the applicability of the proposed strategy: (i) takes into account 5 typical data center architectures that were optimized to conduct the validation process of the proposed strategy; (ii) uses the optimization strategy in four architectures classi ed by ANSI / TIA-942 (TIER I to TIER IV). In both case studies, signi cant improvements were achieved in the results, which were very close to the optimum one that was obtained by a brute force algorithm that analyzes all the possibilities and returns the optimal solution. It is worth mentioning that the time used to obtain the results using the genetic algorithm approach was signi cantly lower (6,763,260 times), in comparison with the strategy which combines all the possible combinations to obtain the optimal result. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-04-19 |
dc.date.accessioned.fl_str_mv |
2019-02-20T13:52:47Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
AUSTRÉGESILO, Márcio Sérgio Soares. Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers. 2018. 116 f. Dissertação (Programa de Pós-Graduação em Informática Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
dc.identifier.uri.fl_str_mv |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7858 |
identifier_str_mv |
AUSTRÉGESILO, Márcio Sérgio Soares. Uma estratégia baseada em modelos e algoritmos genéticos para otimizar arquiteturas de data centers. 2018. 116 f. Dissertação (Programa de Pós-Graduação em Informática Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
url |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7858 |
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por |
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por |
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600 600 600 |
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3671711205811204509 |
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openAccess |
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UFRPE |
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Brasil |
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Universidade Federal Rural de Pernambuco |
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