Adaptive polling intervals for network monitoring
Autor(a) principal: | |
---|---|
Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFPE |
Texto Completo: | https://repositorio.ufpe.br/handle/123456789/29685 |
Resumo: | Monitoring is an essential task for the management of any network and several network functions dependent on it. In Software Defined Networking (SDN) is no different, even with all the benefits provided by it. Network monitoring helps to understand patterns in network traffic, allowing to capture its state and enhance its configuration. Furthermore, it aids the infrastructure management, the discovery of bottlenecks, the location of problems related to software and hardware, and enforces SLAs (Service Level Agreement). Cloud services are spread around the globe and are used constantly by several companies. However, ensure the safety and quality of these services is a difficult and non-stop chore. Network monitoring has a major part in assuring that the services are executed without problem. Ideally, it is expected that the monitoring solution does not overload the network, scales together with the network causing minimal impact, has a controlled resource usage and works for every network device independent of the vendor. The vendor lock-in problem was solved by the OpenFlow protocol, the main and default SDN protocol. However, the others aforementioned problems are dependent on the monitoring solution implementation and used strategies. Furthermore, to the best of our knowledge, no SDN monitoring solution solved these problems ensuring all the needed network monitoring capabilities. In order to address this gap, we proposed SHAMon, a network monitoring solution for Software Defined Networking, created to provide fine-grained and precise information, generating minimal network and resources overhead. We propose the use of the binomial algorithms used for congestion control by the TCP protocol to control our statistics request mechanism. We use all the OpenFlow features to aid in getting timely and refined statistics. Additionally, we use a proxy-server architecture to allow our solution to scale with the network infrastructure. To validate the use of binomial algorithms, we defined a set of experiments. The first set of experiments was intended on analyze two functions of our solution, the former being how variations of parameters of the congestion control algorithms are related to how much the polling time vary. The latter was how variations of the thresholds that limit the execution of the congestion control algorithms are related to the decision to vary the polling time. In addition, we did a comparison experiment with three other solutions, Payless, periodic polling, and OpenNetMon in the same network scenario. The major objective of this experiment was to highlight some of our qualities. The results from both experiments were good, the former showed us the behavior of the binomial algorithms and our solution. Our solution had a low error in network measurement, varying from 26% to 15%. The latter showed that our monitoring overhead was three times lower than the others, validating that our solution is both accurate and not network consuming. |
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MACHADO, Marcos Vinícios da Silvahttp://lattes.cnpq.br/1913759089615606http://lattes.cnpq.br/7532050172035129KELNER, Judith2019-03-13T20:35:35Z2019-03-13T20:35:35Z2017-08-11https://repositorio.ufpe.br/handle/123456789/29685Monitoring is an essential task for the management of any network and several network functions dependent on it. In Software Defined Networking (SDN) is no different, even with all the benefits provided by it. Network monitoring helps to understand patterns in network traffic, allowing to capture its state and enhance its configuration. Furthermore, it aids the infrastructure management, the discovery of bottlenecks, the location of problems related to software and hardware, and enforces SLAs (Service Level Agreement). Cloud services are spread around the globe and are used constantly by several companies. However, ensure the safety and quality of these services is a difficult and non-stop chore. Network monitoring has a major part in assuring that the services are executed without problem. Ideally, it is expected that the monitoring solution does not overload the network, scales together with the network causing minimal impact, has a controlled resource usage and works for every network device independent of the vendor. The vendor lock-in problem was solved by the OpenFlow protocol, the main and default SDN protocol. However, the others aforementioned problems are dependent on the monitoring solution implementation and used strategies. Furthermore, to the best of our knowledge, no SDN monitoring solution solved these problems ensuring all the needed network monitoring capabilities. In order to address this gap, we proposed SHAMon, a network monitoring solution for Software Defined Networking, created to provide fine-grained and precise information, generating minimal network and resources overhead. We propose the use of the binomial algorithms used for congestion control by the TCP protocol to control our statistics request mechanism. We use all the OpenFlow features to aid in getting timely and refined statistics. Additionally, we use a proxy-server architecture to allow our solution to scale with the network infrastructure. To validate the use of binomial algorithms, we defined a set of experiments. The first set of experiments was intended on analyze two functions of our solution, the former being how variations of parameters of the congestion control algorithms are related to how much the polling time vary. The latter was how variations of the thresholds that limit the execution of the congestion control algorithms are related to the decision to vary the polling time. In addition, we did a comparison experiment with three other solutions, Payless, periodic polling, and OpenNetMon in the same network scenario. The major objective of this experiment was to highlight some of our qualities. The results from both experiments were good, the former showed us the behavior of the binomial algorithms and our solution. Our solution had a low error in network measurement, varying from 26% to 15%. The latter showed that our monitoring overhead was three times lower than the others, validating that our solution is both accurate and not network consuming.CAPESVárias funções de rede são dependentes dele. Em Software Defined Networking (SDN) não é diferente, mesmo com todos os benefícios fornecidos pelas redes SDN. O monitoramento de rede ajuda a entender padrões no tráfego, permitindo capturar seu estado e aprimorar sua configuração. Além disso, auxilia no gerenciamento da infra-estrutura, a descoberta de estrangulamentos e garante a aplicação de SLAs (Acordo de Nível de Serviço). Os serviços oferecidos na nuvem estão espalhados pelo mundo e são constantemente utilizados por várias empresas, no entanto, garantir a segurança e qualidade desses serviços é uma tarefa difícil e sem fim. O monitoramento de rede tem uma parte importante em garantir que esses serviços sejam executados sem problemas. Idealmente, espera-se que a solução de monitoramento não sobrecarregue a rede, escale causando impacto mínimo, tenha um uso de recursos controlado e funcione para cada dispositivo de rede independente do fornecedor. O problema de incompatibilidade de dispositivos entre fornecedores foi resolvido pelo protocolo OpenFlow, o principal protocolo SDN. No entanto, os outros problemas mencionados acima dependem da implementação da solução de monitoramento e das estratégias utilizadas. Além disso, nós desconhecemos uma solução de monitoramento para SDN a qual tenha resolvido todos os problemas citados e garanta todas as funções de monitoramento necessárias. Para preencher essa lacuna, propomos SHAMon, uma solução de monitoramento para Redes Definidas por Software, criada para fornecer informações precisas e refinadas, com sobrecarga mínima de rede e recursos. Propomos a utilização dos algoritmos binomiais usados no controle de congestionamento pelo protocolo TCP para controlar nosso mecanismo de solicitação de estatísticas. Utilizamos os recursos do protocolo OpenFlow para auxiliar na obtenção de estatísticas oportunas e refinadas. Além disso, usamos uma arquitetura proxy-servidor para permitir que nossa solução conseguisse escalar com a infraestrutura de rede. Para validar o uso dos algoritmos binomiais, definimos um conjunto de experimentos. O objetivo do primeiro experimento foi analisar dois procedimentos da nossa solução: como as variações de parâmetros dos algoritmos de controle de congestionamento se relacionam com quanto a variação do polling time das estatísticas, e como as variações dos limitantes de execução dos algoritmos de controle de congestionamento se relacionam à decisão de variar o tempo. Nós também comparamos nossa solução com outras três soluções, Payless, polling periódico e OpenNetMon no mesmo cenário de rede. Os resultados de ambos os experimentos foram bons, o primeiro nos mostrou o comportamento dos algoritmos binomiais e da nossa solução. Nossa solução errou pouco na medição da utilização da rede, variando de 26 % a 15 %. No último experimento, vimos que a nossa sobrecarga de rede foi três vezes menor do que os outros, validando que nossa solução é precisa e sobrecarrega pouco a rede.engUniversidade Federal de PernambucoPrograma de Pos Graduacao em Ciencia da ComputacaoUFPEBrasilAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessRedes de computadoresGerenciamento de redesAdaptive polling intervals for network monitoringinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesismestradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPETHUMBNAILDISSERTAÇÃO Marcos Vinicios da Silva Machado.pdf.jpgDISSERTAÇÃO Marcos Vinicios da Silva Machado.pdf.jpgGenerated Thumbnailimage/jpeg1300https://repositorio.ufpe.br/bitstream/123456789/29685/5/DISSERTA%c3%87%c3%83O%20Marcos%20Vinicios%20da%20Silva%20Machado.pdf.jpgf61c861165f65402fdea72fe3f1c03eaMD55ORIGINALDISSERTAÇÃO Marcos Vinicios da Silva Machado.pdfDISSERTAÇÃO Marcos Vinicios da Silva Machado.pdfapplication/pdf5426719https://repositorio.ufpe.br/bitstream/123456789/29685/1/DISSERTA%c3%87%c3%83O%20Marcos%20Vinicios%20da%20Silva%20Machado.pdf7792fe95be0226b2ff910912688b7d7fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.pt_BR.fl_str_mv |
Adaptive polling intervals for network monitoring |
title |
Adaptive polling intervals for network monitoring |
spellingShingle |
Adaptive polling intervals for network monitoring MACHADO, Marcos Vinícios da Silva Redes de computadores Gerenciamento de redes |
title_short |
Adaptive polling intervals for network monitoring |
title_full |
Adaptive polling intervals for network monitoring |
title_fullStr |
Adaptive polling intervals for network monitoring |
title_full_unstemmed |
Adaptive polling intervals for network monitoring |
title_sort |
Adaptive polling intervals for network monitoring |
author |
MACHADO, Marcos Vinícios da Silva |
author_facet |
MACHADO, Marcos Vinícios da Silva |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/1913759089615606 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/7532050172035129 |
dc.contributor.author.fl_str_mv |
MACHADO, Marcos Vinícios da Silva |
dc.contributor.advisor1.fl_str_mv |
KELNER, Judith |
contributor_str_mv |
KELNER, Judith |
dc.subject.por.fl_str_mv |
Redes de computadores Gerenciamento de redes |
topic |
Redes de computadores Gerenciamento de redes |
description |
Monitoring is an essential task for the management of any network and several network functions dependent on it. In Software Defined Networking (SDN) is no different, even with all the benefits provided by it. Network monitoring helps to understand patterns in network traffic, allowing to capture its state and enhance its configuration. Furthermore, it aids the infrastructure management, the discovery of bottlenecks, the location of problems related to software and hardware, and enforces SLAs (Service Level Agreement). Cloud services are spread around the globe and are used constantly by several companies. However, ensure the safety and quality of these services is a difficult and non-stop chore. Network monitoring has a major part in assuring that the services are executed without problem. Ideally, it is expected that the monitoring solution does not overload the network, scales together with the network causing minimal impact, has a controlled resource usage and works for every network device independent of the vendor. The vendor lock-in problem was solved by the OpenFlow protocol, the main and default SDN protocol. However, the others aforementioned problems are dependent on the monitoring solution implementation and used strategies. Furthermore, to the best of our knowledge, no SDN monitoring solution solved these problems ensuring all the needed network monitoring capabilities. In order to address this gap, we proposed SHAMon, a network monitoring solution for Software Defined Networking, created to provide fine-grained and precise information, generating minimal network and resources overhead. We propose the use of the binomial algorithms used for congestion control by the TCP protocol to control our statistics request mechanism. We use all the OpenFlow features to aid in getting timely and refined statistics. Additionally, we use a proxy-server architecture to allow our solution to scale with the network infrastructure. To validate the use of binomial algorithms, we defined a set of experiments. The first set of experiments was intended on analyze two functions of our solution, the former being how variations of parameters of the congestion control algorithms are related to how much the polling time vary. The latter was how variations of the thresholds that limit the execution of the congestion control algorithms are related to the decision to vary the polling time. In addition, we did a comparison experiment with three other solutions, Payless, periodic polling, and OpenNetMon in the same network scenario. The major objective of this experiment was to highlight some of our qualities. The results from both experiments were good, the former showed us the behavior of the binomial algorithms and our solution. Our solution had a low error in network measurement, varying from 26% to 15%. The latter showed that our monitoring overhead was three times lower than the others, validating that our solution is both accurate and not network consuming. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-08-11 |
dc.date.accessioned.fl_str_mv |
2019-03-13T20:35:35Z |
dc.date.available.fl_str_mv |
2019-03-13T20:35:35Z |
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.uri.fl_str_mv |
https://repositorio.ufpe.br/handle/123456789/29685 |
url |
https://repositorio.ufpe.br/handle/123456789/29685 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
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openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pos Graduacao em Ciencia da Computacao |
dc.publisher.initials.fl_str_mv |
UFPE |
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Brasil |
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Universidade Federal de Pernambuco |
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