Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / A adoÃÃo de serviÃos em nuvem està aumentando exponencialmente, e uma das razÃes à porque a sua arquitetura salienta os benefÃcios de serviÃos compartilhados e com pagamento baseado no uso. A computaÃÃo em nuvem possui o foco de proporcionar uma economia em grande escala, possibilitando o acesso a diversos recursos computacionais em tempo real, como serviÃos de aplicaÃÃes, infraestrutura e armazenamento, de modo que estes possam ser obtidos de modo dinÃmico, elÃstico, escalÃvel e rÃpido na medida em que forem consumidos, independente de quem os administra e onde estes recursos estejam alocados. Dentre esses serviÃos, o gerenciamento e armazenamento de dados sÃo componentes crÃticos na pilha de software da nuvem, pois a maioria das aplicaÃÃes sÃo orientadas a dados. Esse serviÃo, conhecido por Database as a Service (DBaaS), nasce como um paradigma de gestÃo de dados, onde um provedor hospeda e gerencia todo ambiente necessÃrio ao funcionamento dos sistemas de banco de dados e o terceiriza como um serviÃo para um ou mais consumidores. PorÃm, ainda hà problemas que impedem a sua adoÃÃo generalizada dos DBaaS. Fornecer serviÃos em nuvem requer procedimentos sofisticados de gestÃo por parte do fornecedor para garantir robustez, desempenho, confiabilidade, seguranÃa, elasticidade e qualidade. Portanto, os consumidores esperam que provedores de DBaaS garantam a qualidade do serviÃo, e lidem com padrÃes dinÃmicos de carga de trabalho e elasticidade, pois à fundamental para garantir que os acordos de nÃvel de serviÃo (SLA) sejam atendidos. No entanto, prover mecanismos de elasticidade, escalabilidade, qualidade de serviÃo e disponibilidade em ambientes em nuvem à um grande desafio. Claramente isto à um desafio tambÃm na disponibilizaÃÃo dos DBaaS, e para se alcanÃar essas funcionalidades e princÃpios à necessÃrio um monitoramento detalhado e preciso. Com isso, esta dissertaÃÃo tem por objetivo a proposta de um framework open-source para o monitoramento de serviÃos de DBaaS, denominado MyDBaaS, cuja finalidade à possibilitar a criaÃÃo de soluÃÃes de monitoramento personalizÃveis e eficientes atravÃs de um modelo de programaÃÃo abrangente e extensÃvel, que disponibiliza desde a definiÃÃo das mÃtricas, procedimento de coleta, recebimento e armazenamento atà mecanismos para consumo das informaÃÃes coletadas em tempo real.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-04-07T12:42:11Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MarceloLima-MestradoCIn-2015.pdf: 2673877 bytes, checksum: 7fed7c87bd80e3d1226642662bc3f739 (MD5) / Made available in DSpace on 2016-04-07T12:42:11Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) MarceloLima-MestradoCIn-2015.pdf: 2673877 bytes, checksum: 7fed7c87bd80e3d1226642662bc3f739 (MD5) Previous issue date: 2015-08-11 / Com a popularização crescente do modelo de computação em nuvem oferecendo serviços em cada uma das camadas de Software-as-a-Service (SaaS), Platform-asa- Service (PaaS) e Infrastructure-as-a-Service (IaaS), começaram a surgir provedores que disponibilizam o serviço específico de Database-as-a-Service (DBaaS), cuja ideia básica é disponibilizar bancos de dados na nuvem. Entretanto, a inviabilidade de execução de operações, consultas e alterações, sobre dados encriptados em serviços DBaaS é um fator que afasta os clientes da possibilidade de levar seus dados para a nuvem. Proprietários de dados e provedores de nuvem anseiam por sistemas criptográficos completamente homomórficos como uma solução. Mas não existe qualquer perspectiva a curto ou médio prazo de que estes sistemas possam ser computacionalmente viáveis. Atualmente pesquisas buscam construir soluções que utilizam sistemas criptográficos viáveis que possibilitem a execução de operações sobre dados encriptados no provedor de DBaaS. Um estudo, precursor e destacado, baseia sua solução em uma arquitetura Proxy, modelo que não é unanimidade para este tipo de solução. Esta pesquisa, baseada em mapeamento sistemático, busca iniciar uma discussão mais profunda sobre modelos de arquitetura para DBaaS e apresenta como principais contribuições: (i) um catálogo de estudos com propostas de soluções, organizado por modelo de arquitetura, (ii) a determinação de uma tendência na escolha de arquiteturas, considerando o estado da arte, (iii) uma investigação de um direcionamento concreto, apontando vantagens e desvantagens, com base nos estudos catalogados, sobre a adoção da arquitetura Proxy em soluções encriptadas de computação em nuvem para DBaaS e (iv) apontar uma lista consistente de questões em aberto acerca das soluções para banco de dados encriptados, com base em dados extraídos dos estudos catalogados. / With the growing popularity of cloud computing model, offering services in each of the layers of Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS), began to emerge providers that provide the specific service Database-as-a-Service (DBaaS), whose basic idea is to provide databases in the cloud. However, the impossibility of executing operations, queries and changes on encrypted data in DBaaS services is a factor that keeps customers the possibility to bring your data to the cloud. Owners of data and cloud providers crave fully homomorphic cryptosystems as a solution. But there is no prospect in the short or medium term that these systems can be computationally feasible. Currently research seek to build solutions using viable cryptographic systems that allow the execution of operations on encrypted data on DBaaS provider. One study, precursor and highlighted, bases its solution on a Proxy architecture model, that is no unanimity for this type of solution. This research, based on systematic mapping, search start a deeper discussion of architectural models for DBaaS and presents as main contributions: (i) a catalog of studies with proposed solutions, organized by architectural model, (ii) the determination of a tendency in choosing architectures, considering the state of the art and (iii) an investigation of a concrete direction, pointing advantages and disadvantages, based on cataloged studies, on the adoption of Proxy architecture over cloud computing encrypted solutions to DBaaS and (iv) point to a consistent list of open questions about the solutions for encrypted database, based on data extracted from cataloged studies.

Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. The tight coupling of data and processing thereon is implemented in different systems in a variety of application scenarios such as data analysis, database-as-a-service, and data management on multiprocessor systems. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies, i.e., methods that decide the mapping from tasks to nodes, are core components in data-oriented systems. Good allocation strategies can lead to balanced systems while bad allocation strategies cause skew in the load and therefore suboptimal application performance and infrastructure utilization. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. To ensure the scalability of data-oriented systems and to keep them manageable with hundreds of thousands of tasks, thousands of nodes, and dynamic workloads, fast and reliable allocation strategies are mandatory. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms. Therefore, we show that systems from different application scenarios with different abstraction levels can be generalized to generic infrastructure and workload descriptions. We use weighted graph representations to model infrastructures with bounded and unbounded, i.e., overcommited, resources and possibly non-linear performance characteristics. Based on our generalized infrastructure and workload model, we formalize the allocation problem, which seeks valid and balanced allocations that minimize communication. Our allocation strategies partition the workload graph using solution heuristics that work with single and multiple vertex weights. Novel extensions to these solution heuristics can be used to balance penalized and secondary graph partition weights. These extensions enable the allocation strategies to handle infrastructures with non-linear performance behavior. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end--performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods.

A Multi-tenant database (MTD) is a way of deploying a Database as a Service (DaaS). A multi-tenant database refers to a principle where a single instance of a Database Management System (DBMS) runs on a server, serving multiple clients organisations (tenants). This technology has helped to discard the large-scale investments in hardware and software resources, in upgrading them regularly and in expensive licences of application software used on in-house hosted database systems. This is gaining momentum with significant increase in the number of organisations ready to take advantage of the technology. The benefits of MTD are potentially enormous but for any organisation to venture into its adoption, there are some salient factors which must be well understood and examined before venturing into the concept. This research examines these factors, different models of MTD, consider the requirements and challenges of implementing MTDs. Investigation of the degree of impact each of these factors has on the adoption of MTD is conducted in this research which focused mainly on public organisations. The methodology adopted in undertaking this study is a mixed method which involved both qualitative and quantitative research approaches. These strategies are used here to cover statistics (quantifiable data) and experts’ knowledge and experiences (abstract data) in order to satisfactorily achieve the aim and objectives and complete the research. Following the involvement of these strategies, a framework was developed and further refined after a second survey was carried out with a quantitative approach. This framework will help prospective tenants to make informed decisions about the adoption of the concept. The research also considers the direction of decisions about MTDs in situations where two or more factors are combined. A new MTD framework is presented that improves the decision making process of MTD adoption. Also, an Expert System (ES) is developed from the framework which was validated via a survey and analysed with the aid of SPSS software. The findings from the validation indicated that the framework is valuable and suitable for use in practice since majority of respondents accepted the research findings and recommendations for success. Likewise, the ES was validated with majority of participants accepting it and embracing the high level of its friendliness.

Data orientation is a common design principle in distributed data management systems. In contrast to process-oriented or transaction-oriented system designs, data-oriented architectures are based on data locality and function shipping. The tight coupling of data and processing thereon is implemented in different systems in a variety of application scenarios such as data analysis, database-as-a-service, and data management on multiprocessor systems. Data-oriented systems, i.e., systems that implement a data-oriented architecture, bundle data and operations together in tasks which are processed locally on the nodes of the distributed system. Allocation strategies, i.e., methods that decide the mapping from tasks to nodes, are core components in data-oriented systems. Good allocation strategies can lead to balanced systems while bad allocation strategies cause skew in the load and therefore suboptimal application performance and infrastructure utilization. Optimal allocation strategies are hard to find given the complexity of the systems, the complicated interactions of tasks, and the huge solution space. To ensure the scalability of data-oriented systems and to keep them manageable with hundreds of thousands of tasks, thousands of nodes, and dynamic workloads, fast and reliable allocation strategies are mandatory. In this thesis, we develop novel allocation strategies for data-oriented systems based on graph partitioning algorithms. Therefore, we show that systems from different application scenarios with different abstraction levels can be generalized to generic infrastructure and workload descriptions. We use weighted graph representations to model infrastructures with bounded and unbounded, i.e., overcommited, resources and possibly non-linear performance characteristics. Based on our generalized infrastructure and workload model, we formalize the allocation problem, which seeks valid and balanced allocations that minimize communication. Our allocation strategies partition the workload graph using solution heuristics that work with single and multiple vertex weights. Novel extensions to these solution heuristics can be used to balance penalized and secondary graph partition weights. These extensions enable the allocation strategies to handle infrastructures with non-linear performance behavior. On top of the basic algorithms, we propose methods to incorporate heterogeneous infrastructures and to react to changing workloads and infrastructures by incrementally updating the partitioning. We evaluate all components of our allocation strategy algorithms and show their applicability and scalability with synthetic workload graphs. In end-to-end--performance experiments in two actual data-oriented systems, a database-as-a-service system and a database management system for multiprocessor systems, we prove that our allocation strategies outperform alternative state-of-the-art methods.

Growing number of applications store data in relational databases. Moving database applications to the cloud faces challenges related to flexible and scalable management of data. The obvious strategy of hosting legacy database management systems (DMBSs) on virtualized cloud resources leads to sub optimal utilization and performance. However, the layered architecture inside the DBMS allows for virtualization and consolidation above the OS level which can lead to significantly better system utilization and application performance. Finding an optimal database cloud solution requires finding an assignment from virtual to physical resources as well as configurations for all components. Our goal is to provide a virtualization advisor that aids in setting up and operating a database cloud. By formulating analytic cost, workload, and resource models performance of cloud-hosted relational database services can be significantly improved.

Databases as a service (DBaaS) transfer the advantages of cloud computing to data management systems, which is important for the big data era. The allocation in a DBaaS system, i.e., the mapping from databases to nodes of the infrastructure, influences performance, utilization, and cost-effectiveness of the system. Modeling databases and the underlying infrastructure as weighted graphs and using graph partitioning and mapping algorithms yields an allocation strategy. However, graph partitioning assumes that individual vertex weights add up (linearly) to partition weights. In reality, performance does usually not scale linearly with the amount of work due to contention on the hardware, on operating system resources, or on DBMS components. To overcome this issue, we propose an allocation strategy based on penalized graph partitioning in this paper. We show how existing algorithms can be modified for graphs with non-linear partition weights, i.e., vertex weights that do not sum up linearly to partition weights. We experimentally evaluate our allocation strategy in a DBaaS system with 1,000 databases on 32 nodes.