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Proposta e d esenvolvimento de uma arquitetura para obtenção de QoS em uma Grade de serviços

Proposta e d esenvolvimento de uma arquitetura para obtenção de QoS em uma Grade de serviços. Maycon Leone M. Peixoto Profa . Dra . Regina H. C. Santana. Viagem : Qatar. Outline. Is possible to use Grid Computing as IaaS of Cloud ?

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Proposta e d esenvolvimento de uma arquitetura para obtenção de QoS em uma Grade de serviços

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  1. Proposta e desenvolvimento de umaarquiteturaparaobtenção de QoS emuma Grade de serviços

    Maycon Leone M. Peixoto Profa. Dra. Regina H. C. Santana
  2. Viagem : Qatar
  3. Outline Is possible to use Grid Computing as IaaSofCloud? Yes, if you look at all physical resources (either the datacenters or virtual organization) as virtualized resources.
  4. Background Web 2.0 is targeted for service-oriented, where is also Cloud computing. Supercomputing and Cluster Computing are not concerned with service-oriented applications. - Grid covers many fields and it is overlap with Cloud mainly in cases of lesser scale. I. Foster, Y. Zhao, I. Raicu, and S. Lu, “Cloud computing and grid computing 360-degree compared,”
  5. Introdução A computação nas nuvens utiliza o modelo “pay-as-you-go“. Para adoção desse modelo a abordagem de utilizar os recursos virtualizados tem grande importância. Promessas: Reduzir custos, melhorar a entrega dos serviços, inovação na forma de fazer negócios.
  6. Focus The goal is to reduce the cost of computing, increase reliability, and increase flexibility by transforming computers from something that we buy and operate ourselves to something that is operated by a third party. There is a common need to be able to manage large facilities; to define methods by which consumers discover, request, and use resources provided by the central facilities; When services are used, a mechanism is necessary to identify the ways that cover their needs with respect to QoS. The use of a Metascheduler would improve some QoS metrics, such as: performance and reliability.
  7. Related Works
  8. Tendências
  9. Tendências
  10. MACC´s This architecture extends architectures to the Grid. Allows the growing system be autonomous and scalable. The idea is to use the MACC to perform two functions: To control partial overload in the service network. To improve the performance in query for services in the connectivity graph.
  11. TheArchitectureLayers
  12. MACC´s
  13. MACC´s BymeansofWorkloadEngine in Metascheduler is possible to do anestimationabouttheloadonresources. It´s known all services duration within VO because it learns from the past experience. This estimation happens for each rate utilization; The delta is used to make a gap between the estimated value and requested value of QoS by the client.
  14. Ambiente de Simulação: CloudSim
  15. Planejamento de Experimentos Fatorese níveis: Número de serviçosporusuário. 10 e 30 Número de usuários. 5 e 10 Algoritmo de roteamento de serviços. RR (Round Robin) e CB (Capacity Based) Algoritmo de alocação de máquina virtual. E (Experience) e R (Random)
  16. Planejamento de Experimentos Variáveis de Resposta Custo Tempo médio de resposta Confiabilidade Fixos: Número de Datacenters: 3 Número de recursosemcada Datacenter: 100 Heterogeneidade entre Datacenters (1x, 2x, 3x) Preço.
  17. Planejamento de Experimentos: FatorialCompleto
  18. Planejamento de Experimentos Como mencionado anteriormente, este projeto realiza a análise com base no modelo de regressão. São considerados quatro fatores , resultando em 16 experimentos, y1; y2; y3; :::; y16. Substituindo-se as observações no modelo, obtêm-se os valores de qA; qB; qC; qD; qAB; qAC; qAD; qBC; qBD; qCD; qABC; qABD; qACD; qBCD; qABCD. Por exemplo, q0, como segue na equação. Cada parâmetro deste informa o valor obtido pelas variáveis de reposta.
  19. Planejamento de Experimentos
  20. Planejamento de Experimentos A partir dos valores de qA; qB; qC; qD; qAB; qAC; qAD; qBC; qBD; qCD; qABC; qABD; qACD; qBCD; qABCD pode-se determinar soma dos quadrados. A soma dos quadrados fornecerá a variação total das variáveis de resposta mensuradas e consequentemente a influência de cada fator e suas interações. Para obter SST é aplicada a equação:
  21. Planejamento de Experimentos
  22. Influência dos Fatores
  23. Análise: Algoritmo de Alocação de VM
  24. Análise: Algoritmo de Alocação de VM
  25. Análise: Algoritmo de Roteamento
  26. Final Remarks This paper has shown a framework to cover a business model - SOA based - for the Cloud environment. It works with a Metascheduler By means of MACC is possible to express a business model built on a Cloud service. This business model deals with the management of services which can take into account issues related to the infrastructure of a Grid service, extending the domain and scope of applications of the participants.
  27. Fim.
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