Proposta e desenvolvimento 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? – Yes, if you look at all physical resources (either the datacenters or virtual organization) as virtualized resources. 3

Background I. Foster, Y. Zhao, I. Raicu, and S. Lu, Cloud computing and grid computing 360-degree compared, - 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. 4

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.

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.

Related Works 7

Tendências

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: 1)To control partial overload in the service network. 2)To improve the performance in query for services in the connectivity graph. 10

The Architecture Layers 11

MACC´s 12

MACC´s By means of Workload Engine in Metascheduler is possible to do an estimation about the load on resources. 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. 13

Ambiente de Simulação: CloudSim

Planejamento de Experimentos Fatores e níveis: – Número de serviços por usuário. 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)

Planejamento de Experimentos Variáveis de Resposta – Custo – Tempo médio de resposta – Confiabilidade Fixos: – Número de Datacenters: 3 – Número de recursos em cada Datacenter: 100 – Heterogeneidade entre Datacenters (1x, 2x, 3x) – Preço.

Planejamento de Experimentos: Fatorial Completo

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.

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:

Planejamento de Experimentos

Influência dos Fatores

Análise: Algoritmo de Alocação de VM

Análise: Algoritmo de Roteamento

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. 26

Fim.