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You searched for subject:(Virtual machines provisioning). Showing records 1 – 2 of 2 total matches.

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1. Paulo Antonio Leal Rego. FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento.

Degree: Master, 2012, Universidade Federal do Ceará

O escalonamento de recursos à um processo chave para a plataforma de ComputaÃÃo em Nuvem, que geralmente utiliza mÃquinas virtuais (MVs) como unidades de escalonamento. O uso de tÃcnicas de virtualizaÃÃo fornece grande flexibilidade com a habilidade de instanciar vÃrias MVs em uma mesma mÃquina fÃsica (MF), modificar a capacidade das MVs e migrÃ-las entre as MFs. As tÃcnicas de consolidaÃÃo e alocaÃÃo dinÃmica de MVs tÃm tratado o impacto da sua utilizaÃÃo como uma medida independente de localizaÃÃo. à geralmente aceito que o desempenho de uma MV serà o mesmo, independentemente da MF em que ela à alocada. Esta à uma suposiÃÃo razoÃvel para um ambiente homogÃneo, onde as MFs sÃo idÃnticas e as MVs estÃo executando o mesmo sistema operacional e aplicativos. No entanto, em um ambiente de ComputaÃÃo em Nuvem, espera-se compartilhar um conjunto composto por recursos heterogÃneos, onde as MFs podem variar em termos de capacidades de seus recursos e afinidades de dados. O objetivo principal deste trabalho à apresentar uma arquitetura que possibilite a padronizaÃÃo da representaÃÃo do poder de processamento das MFs e MVs, em funÃÃo de Unidades de Processamento (UPs), apoiando-se na limitaÃÃo do uso da CPU para prover isolamento de desempenho e manter a capacidade de processamento das MVs independente da MF subjacente. Este trabalho busca suprir a necessidade de uma soluÃÃo que considere a heterogeneidade das MFs presentes na infraestrutura da Nuvem e apresenta polÃticas de escalonamento baseadas na utilizaÃÃo das UPs. A arquitetura proposta, chamada FairCPU, foi implementada para trabalhar com os hipervisores KVM e Xen, e foi incorporada a uma nuvem privada, construÃda com o middleware OpenNebula, onde diversos experimentos foram realizados para avaliar a soluÃÃo proposta. Os resultados comprovam a eficiÃncia da arquitetura FairCPU em utilizar as UPs para reduzir a variabilidade no desempenho das MVs, bem como para prover uma nova maneira de representar e gerenciar o poder de processamento das MVs e MFs da infraestrutura.

Resource scheduling is a key process for cloud computing platform, which generally uses virtual machines (VMs) as scheduling units. The use of virtualization techniques provides great flexibility with the ability to instantiate multiple VMs on one physical machine (PM), migrate them between the PMs and dynamically scale VMâs resources. The techniques of consolidation and dynamic allocation of VMs have addressed the impact of its use as an independent measure of location. It is generally accepted that the performance of a VM will be the same regardless of which PM it is allocated. This assumption is reasonable for a homogeneous environment where the PMs are identical and the VMs are running the same operating system and applications. Nevertheless, in a cloud computing environment, we expect that a set of heterogeneous resources will be shared, where PMs will face changes both in terms of their resource capacities and as also in data affinities. The main objective of this work is to propose an architecture…

Advisors/Committee Members: Miguel Franklin de Castro, Josà Neuman de Souza, Bruno Richard Schulze, Paulo Henrique Mendes Maia.

Subjects/Keywords: CIENCIA DA COMPUTACAO; Provisionamento de mÃquinas virtuais; Ambiente heterogÃneo; LimitaÃÃo do uso da CPU; ComputaÃÃo em nuvem; VirtualizaÃÃo; Escalonamento; Unidade de processamento; Variabilidade de desempenho; KVM; Xen; OpenNebula; Virtual machines provisioning; Heterogeneous environment; Limit CPU usage; Cloud computing; Virtualization: Scheduling; Processing unit; Performance variability; KVM; Xen; OpenNebula; ComputaÃÃo em nuvem; Processamento eletrÃnico de dados - Processamento distribuido; MÃquinas leitoras (Processamento de dados)

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Rego, P. A. L. (2012). FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento. (Masters Thesis). Universidade Federal do Ceará. Retrieved from http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;

Chicago Manual of Style (16th Edition):

Rego, Paulo Antonio Leal. “FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento.” 2012. Masters Thesis, Universidade Federal do Ceará. Accessed November 23, 2020. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;.

MLA Handbook (7th Edition):

Rego, Paulo Antonio Leal. “FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento.” 2012. Web. 23 Nov 2020.

Vancouver:

Rego PAL. FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento. [Internet] [Masters thesis]. Universidade Federal do Ceará 2012. [cited 2020 Nov 23]. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;.

Council of Science Editors:

Rego PAL. FairCPU: Uma Arquitetura para Provisionamento de MÃquinas Virtuais Utilizando CaracterÃsticas de Processamento. [Masters Thesis]. Universidade Federal do Ceará 2012. Available from: http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7653 ;

2. Darrous, Jad. Scalable and Efficient Data Management in Distributed Clouds : Service Provisioning and Data Processing : Gestion de données efficace et à grande échelle dans les clouds distribués : Déploiement de services et traitement de données.

Degree: Docteur es, Informatique, 2019, Lyon

Cette thèse porte sur des solutions pour la gestion de données afin d'accélérer l'exécution efficace d'applications de type « Big Data » (très consommatrices en données) dans des centres de calculs distribués à grande échelle. Les applications de type « Big Data » sont de plus en plus souvent exécutées sur plusieurs sites. Les deux principales raisons de cette tendance sont 1) le déplacement des calculs vers les sources de données pour éliminer la latence due à leur transmission et 2) le stockage de données sur un site peut ne pas être réalisable à cause de leurs tailles de plus en plus importantes.La plupart des applications s'exécutent sur des clusters virtuels et nécessitent donc des images de machines virtuelles (VMI) ou des conteneurs d’application. Par conséquent, il est important de permettre l’approvisionnement rapide de ces services afin de réduire le temps d'attente avant l’exécution de nouveaux services ou applications. Dans la première partie de cette thèse, nous avons travaillé sur la récupération et le placement des données, en tenant compte de problèmes difficiles, notamment l'hétérogénéité des connexions au réseau étendu (WAN) et les besoins croissants en stockage pour les VMIs et les conteneurs d’application.Par ailleurs, les applications de type « Big Data » reposent sur la réplication pour fournir des services fiables et rapides, mais le surcoût devient de plus en plus grand. La seconde partie de cette thèse constitue l'une des premières études sur la compréhension et l'amélioration des performances des applications utilisant la technique, moins coûteuse en stockage, des codes d'effacement (erasure coding), en remplacement de la réplication.

This thesis focuses on scalable data management solutions to accelerate service provisioning and enable efficient execution of data-intensive applications in large-scale distributed clouds. Data-intensive applications are increasingly running on distributed infrastructures (multiple clusters). The main two reasons for such a trend are 1) moving computation to data sources can eliminate the latency of data transmission, and 2) storing data on one site may not be feasible given the continuous increase of data size.On the one hand, most applications run on virtual clusters to provide isolated services, and require virtual machine images (VMIs) or container images to provision such services. Hence, it is important to enable fast provisioning of virtualization services to reduce the waiting time of new running services or applications. Different from previous work, during the first part of this thesis, we worked on optimizing data retrieval and placement considering challenging issues including the continuous increase of the number and size of VMIs and container images, and the limited bandwidth and heterogeneity of the wide area network (WAN) connections.On the other hand, data-intensive applications rely on replication to provide dependable and fast services, but it became expensive and even infeasible with the unprecedented growth of data size. The second part…

Advisors/Committee Members: Pérez, Christian (thesis director).

Subjects/Keywords: Gestion de données; Systèmes de stockage à grande échelle; Clouds geo-distribués; Edge/Fog computing; Déploiement de services; Images des machines virtuelles et des conteneurs; Hadoop; Codage d'effacement; Data Management; Large-scale storage systems; Geo-distributed Clouds; Edge/Fog computing; Service provisioning; Virtual machine and container images; Hadoop; Erasure coding

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Darrous, J. (2019). Scalable and Efficient Data Management in Distributed Clouds : Service Provisioning and Data Processing : Gestion de données efficace et à grande échelle dans les clouds distribués : Déploiement de services et traitement de données. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2019LYSEN077

Chicago Manual of Style (16th Edition):

Darrous, Jad. “Scalable and Efficient Data Management in Distributed Clouds : Service Provisioning and Data Processing : Gestion de données efficace et à grande échelle dans les clouds distribués : Déploiement de services et traitement de données.” 2019. Doctoral Dissertation, Lyon. Accessed November 23, 2020. http://www.theses.fr/2019LYSEN077.

MLA Handbook (7th Edition):

Darrous, Jad. “Scalable and Efficient Data Management in Distributed Clouds : Service Provisioning and Data Processing : Gestion de données efficace et à grande échelle dans les clouds distribués : Déploiement de services et traitement de données.” 2019. Web. 23 Nov 2020.

Vancouver:

Darrous J. Scalable and Efficient Data Management in Distributed Clouds : Service Provisioning and Data Processing : Gestion de données efficace et à grande échelle dans les clouds distribués : Déploiement de services et traitement de données. [Internet] [Doctoral dissertation]. Lyon; 2019. [cited 2020 Nov 23]. Available from: http://www.theses.fr/2019LYSEN077.

Council of Science Editors:

Darrous J. Scalable and Efficient Data Management in Distributed Clouds : Service Provisioning and Data Processing : Gestion de données efficace et à grande échelle dans les clouds distribués : Déploiement de services et traitement de données. [Doctoral Dissertation]. Lyon; 2019. Available from: http://www.theses.fr/2019LYSEN077

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