Extending a methodology for migration of the database layer to the cloud considering relational database schema migration to NoSQL

The advances in Cloud computing and in modern Web applications have raised the need for highly available and scalable distributed databases to accommodate the big data being created and consumed. Along with the explosion in data growth comes the necessity to rapidly evolve databases and schemas to meet user demands for new functionality. A special attention is being paid to the vast amounts of semi-structured and un-structured data, and the data management tools should reflect the support for these needs. This has lead to the development of new Cloud serving systems such as "Not Only" SQL (NoSQL) databases. NoSQL databases were driven by the scalability needs of the big companies, such as Google, Facebook, Amazon, and Yahoo. While the demands of these key players are different from those of small and medium enterprises in terms of scalability, the core problem is the same - storage arrays are not scalable and force you into expensive, forklift upgrades. These facts combined with changes in how IT resources are delivered and consumed through the Cloud computing paradigm, projects adopting NoSQL solutions are not a hype anymore. NoSQL databases are being offered as a service by the big Cloud providers, such as Google, Amazon, Microsoft, but by smaller vendors as well. In this master thesis we investigate the possibilities and limitations of mapping relational database schemas to NoSQL schemas when migrating the database layer to the Cloud. Based on literature research we provide recommendations and guidelines with regard to schema transformation and discuss the implications at other application architecture layers, such as business logic and data access layer. We extend an existing data migration tool and methodology for incorporating the migration guidelines and hints. Moreover, we validate our work based on a chosen sub-set of relational and NoSQL databases by using example data from the established TPC-H benchmark

Efficient adaptive query processing on large database systems available in the cloud environment

Tese de Doutoramento em InformáticaNowadays, many companies are migrating their applications and data to cloud service providers, mainly because of their ability to answer quickly to business requirements. Thereby, the performance is an important requirement for most customers when they wish to migrate their applications to the cloud. Therefore, in cloud environments, resources should be acquired and released automatically and quickly at runtime. Moreover, the users and service providers expect to get answers in time to ensure the service SLA (Service Level Agreement). Consequently, ensuring the QoS (Quality of Service) is a great challenge and it increases when we have large amounts of data to be manipulated in this environment. To resolve this kind of problems, several researches have been focused on shorter execution time using adaptive query processing and/or prediction of resources based on current system status. However, they present important limitations. For example, most of these works does not use monitoring during query execution and/or presents intrusive solutions, i.e. applied to the particular context. The aim of this thesis is the development of new solutions/strategies to efficient adaptive query processing on large databases available in a cloud environment. It must integrate adaptive re-optimization at query runtime and their costs are based on the SRT (Service Response Time – SLA QoS performance parameter). Finally, the proposed solution will be evaluated on large scale with large volume of data, machines and queries in a cloud computing infrastructure. Finally, this work also proposes a new model to estimate the SRT for different request types (database access requests). This model will allow the cloud service provider and its customers to establish an appropriate SLA relative to the expected performance of the services available in the cloud.Atualmente, muitas companhias têm migrado suas aplicações e dados para fornecedores de serviços em nuvem, pois um dos principais benefícios dessa tecnologia é a capacidade de responder rapidamente às necessidades do negócio. Assim, o desempenho é um dos mais importantes requisitos para a maioria dos clientes que desejam migrar suas aplicações para a nuvem. Em ambiente de nuvem, os recursos devem ser adquiridos e libertados automaticamente e rapidamente em tempo de execução. Além disso, os utilizadores e fornecedores de serviços esperam sempre garantir o contrato SLA (Acordo de Nível de Serviço). Consequentemente, garantir o QoS (Qualidade de Serviço) é um grande desafio, que se torna mais complexo quando existe uma grande quantidade de dados a serem manipulados neste ambiente. Para resolver estes tipos de problemas, diversas pesquisas têm sido realizadas focando o menor tempo de execução dos pedidos do utilizador na nuvem usando técnicas de processamento adaptativo de consultas e/ou utilizando técnicas de predição de recursos baseados no estado atual do sistema. Contudo, esses trabalhos apresentam limitações importantes. Por exemplo, a maioria desses trabalhos não utiliza monitorazação durante a execução da consulta e/ou apresenta soluções intrusivas, isto é, aplicadas a um contexto particular. Portanto, o objetivo desta tese consiste no desenvolvimento de uma nova solução/estratégia para o processamento eficiente (adaptativo) de consultas sobre grandes bases de dados disponíveis em ambiente de nuvem. Ela irá integrar técnicas de otimização adaptativas em tempo de execução da consulta e seus custos são baseados no SRT (Tempo de Resposta do Serviço – parâmetro QoS de desempenho do SLA). A solução proposta será avaliada em larga escala utilizando uma grande base de dados, máquinas e consultas em um ambiente real de computação na nuvem. Finalmente, este trabalho também propõe um novo modelo para estimar o SRT para diferentes tipos de pedidos (pedidos de acesso a banco de dados). Este modelo permitirá que um fornecedor de serviços em nuvem e seus clientes possam estabelecer um contrato SLA adequado, relativo ao desempenho esperado dos serviços disponíveis em nuvem

Hyperscale Data Processing With Network-Centric Designs

Today’s largest data processing workloads are hosted in cloud data centers. Due to unprecedented data growth and the end of Moore’s Law, these workloads have ballooned to the hyperscale level, encompassing billions to trillions of data items and hundreds to thousands of machines per query. Enabling and expanding with these workloads are highly scalable data center networks that connect up to hundreds of thousands of networked servers. These massive scales fundamentally challenge the designs of both data processing systems and data center networks, and the classic layered designs are no longer sustainable. Rather than optimize these massive layers in silos, we build systems across them with principled network-centric designs. In current networks, we redesign data processing systems with network-awareness to minimize the cost of moving data in the network. In future networks, we propose new interfaces and services that the cloud infrastructure offers to applications and codesign data processing systems to achieve optimal query processing performance. To transform the network to future designs, we facilitate network innovation at scale. This dissertation presents a line of systems work that covers all three directions. It first discusses GraphRex, a network-aware system that combines classic database and systems techniques to push the performance of massive graph queries in current data centers. It then introduces data processing in disaggregated data centers, a promising new cloud proposal. It details TELEPORT, a compute pushdown feature that eliminates data processing performance bottlenecks in disaggregated data centers, and Redy, which provides high-performance caches using remote disaggregated memory. Finally, it presents MimicNet, a fine-grained simulation framework that evaluates network proposals at datacenter scale with machine learning approximation. These systems demonstrate that our ideas in network-centric designs achieve orders of magnitude higher efficiency compared to the state of the art at hyperscale

Exploring the development of a framework for agile methodologies to promote the adoption and use of cloud computing services in South Africa

The emergence of cloud computing is influencing how businesses develop, re-engineer, and implement critical software applications. The cloud requires developers to elevate the importance of compliance with security policies, regulations and internal engineering standards in their software development life cycles. Cloud computing and agile development methodologies are new technologies associated with new approaches in the way computing services are provisioned and development of quality software enhanced. However adoption and use of agile and cloud computing by SMMEs in South Africa is seemingly constrained by a number of technical and non-technical challenges. Using Grounded Theory and case study method this study was aimed at exploring the development of a framework for agile methodologies to promote the adoption and use of cloud computing services by SMMEs in South Africa. Data was collected through survey and in-depth interviews. Open, Axial and Selective coding was used to analyse the data. In tandem with its main objective the study, besides exploring the development of the envisaged framework, also generated and made available valuable propositions and knowledge that SMMEs in South Africa using agile development methodologies can use to work better with cloud computing services in the country without compromising on software quality. The findings of this study and the emerging insights around the development of the framework, which in itself also constitutes an important decision making tool for supporting adoption and use of cloud computing services, are a substantial contribution to knowledge and practice in the ICT field of information systems in South AfricaInformation ScienceD. Phil. (Information Systems

The role of cloud computing in addressing small, medium enterprise challenges in South Africa

This thesis was motivated by Roberts (2010) who found that 63% of SMEs in South Africa do not make it past second year of operation. To expand further on this problem, we reviewed literature to understand key business challenges experienced by SMEs in South Africa which contribute to this high failure rate. The challenges include red tape, labour legislation, lack of skills, lack of innovation, impact of crime, and lack of funds. The research project aimed to answer a key question: “How can information technology, in the form of Cloud Computing be used to address the challenges faced by small and medium businesses in South Africa?” To answer this question, data was collected from 265 SME companies and quantitatively analysed. It is important to note that the profile of SMEs targeted in this study are those that employed fewer than 200 employees, with a turnover of not less than 26 million rand per annum, and registered with South African Revenue Services (SARS) and also with the Companies and Intellectual Property Commission (CIPC) of South Africa. Over 60% of the firms that responded to the survey were in business for more than 10 years which means we are mainly dealing with data from businesses that have past the survivalist stage and are matured businesses. These are businesses that can share their experiences and challenges they faced throughout their journey. The profile of SMEs in this study should not be confused with that of Very Small Medium Enterprise Businesses. The questionnaire was designed to address four themes being the Demographic profile, SME Business Environment, Threat of Survival, and lastly Technology Adoption. Key finding in this research is that 60% of the panellists stated that red tape is the overriding challenge that small businesses contend with. 67% of the panellists confirmed that they have not invested in their businesses in the past year; and 53% stated that they have not applied for finance from the bank for fear of being rejected. Only 30% of the SME market were found to use enterprise resource planning (ERP) and 62% do not have their own IT department. Of great concern is that 65% of the panellists have experienced server down time at least once in the past year. Inability to predict the rising IT costs in a firm has been cited as the main concern when running IT on premise. The cost predictability finding was also discovered to be a benefit enjoyed by the SMEs who use Cloud Computing. The conclusion is that there is a relationship between Cloud Computing, Small and Medium Enterprise businesses and the challenges they face in their business environment. To address the identified business challenges, technology adoption studies by Gumbi & Mnkandla (2015), Carcary, Doherty & Conway (2014), Lacovou et al (1995), Mohlomeane & Ruxwana (2014), Kshetri (2010), BMI Research (2018), Conway & Curry (2012), Li, Zhao & Yu (2015), Wernefeldt (1985), Schindehuitte & Morris (2001), Tornatzy & Flesher (1991) were reviewed. From these publications, the Technology, Organisational and Environmental (TOE) was found to be relevant and of interest for use in answering the main research question. This study developed the Cloud Adoption Framework which is the anchor of all SME challenges. Key study contribution is that the TOE model, which is predominantly used to understand the determinants of technology adoption like various industry applications, infrastructure innovations etc., are now used to address specific challenges that have contributed in the high failure rate of SME business. This is the first-time TOE model has been used to align with key SME challenges that contribute to firms’ failure. Specific technology across Software, Infrastructure and Platform services models are recommended for use by SMEs to ensure challenges are mitigated and improve the chances of survival for SMEs operating in South Africa. By following the recommended Cloud Adoption Framework, SMEs should be able to navigate the complexities brought about by the tough operating environment and also the technologies available to address those challenges. All six challenges have solutions in Cloud Computing and SMEs are educated on these solutions and also how to access these on a pay as you use model of consumption.Business ManagementD.B.L

Panoramic and main features of Business Analytics

This thesis investigates the world of Business Analytics, an emerging set of trends, technologies, practices that has started to attract the attentions of a growing number of companies who recognize its value. Starting with the denition of what Business Analytics is, its description and state of the art, the thesis explains its most relevant features, depicts the characteristics of a much more \intelligent" enterprise, guides the reader through a sort of "how-to" manual to make a company more analytical with particular attention to the type of company, unveils both current and future techniques and trends, presents cases of companies who have successfully implemented and embraced an analytical cultur

The e-Government Development Discourse

research agenda for e-Government. When e-Government was first conceived, it was designed upon basic technologies where the emphasis was only on the simple display of government information for citizens to read. Nowadays, e-Government design comprises many complicated modules such as upload and download consoles, two-way interaction consoles between citizens and government agents, integrated government business processes presenting the whole of government, and it does not depend solely on technology. The complexity of e-Government has now evolved to include political, cultural, economic, social and technical dimensions. Bringing all these difficult aspects together is so complicated that it needs carefully planned strategies informed by local contextual characteristics. Rather than giving formulaic definitions and conceptual standpoints on many aspects of e-Government, as is the case in many e-Government publications, this book will explore the frontiers of global knowledge value chains by discussing current and future dimensions of e-Government. For example, the book discusses the concept of data governance by exploring how actual opening up of government data can be achieved, especially in a developing world context. Further, the book posits that opening government data should be followed by the opening up of government business processes in order to peddle the concept of accountability and responsiveness. Much text on data governance has concentrated on articulating the basic definitions surrounding this concept. Another very important topic explored in this book is regarding how the concept of decolonisation can be extended to e-Government by providing practical examples as to how researchers in the developing world can contribute to the advancement of e-Government as a scientific field of enquiry and guide its implementation, thereof. Decolonisation is advocated for in e-Government research so that there is a balance in the inclusion of the Afrocentric knowledge into e-Government advancement other than over-reliance on the Euro-, Asia- and America-centric knowledge value chains (Mbembe 2015). As e-Government is a very expensive undertaking, the issue of funding has excluded African countries and a majority of the developing world from implementing e-Government. Despite funding being a critical cornerstone of e-Government development, there is a dearth of information on this topic. Therefore, this book provides a chapter which discusses traditional and innovative ways of funding e-Government design and implementation which can go a long way in improving e-Government penetration into the developing world. Further, the book explores how intelligent e-Government applications can be designed, especially in resource-constrained countries. A couple of emerging technology innovations such as fog computing and intelligent information technology are explored within the realm of e-Government design