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The growing disparity in data storage and retrieval needs of modern applications is driving the proliferation of a wide variety of storage systems (e.g., key-value stores, cloud storage services, distributed filesystems, and flash cache, etc.). While extant storage systems are designed and tuned for a specific set of applications targeting a range of workload characteristics, they lack the flexibility in adapting to the ever-changing workload behaviors. Moreover, the complexities in implementing modern storage systems and adapting ever-changing storage requirements present unique opportunities and engineering challenges. In this dissertation, we design and develop a series of novel data management and storage systems solutions by applying a simple yet effective rule---workload awareness. We find that simple workload-aware data management strategies are effective in improving the efficiency of modern storage systems, sometimes by an order of magnitude. The first two works tackle the data management and storage space allocation issues at distributed and cloud storage level, while the third work focuses on low-level data management problems in the local storage system, which many high-level storage/data-intensive applications rely on. In the first part of this dissertation (Chapter 3), we propose and develop MBal, a high-performance in-memory object caching framework with adaptive multi-phase load balancing, which supports not only horizontal (scale-out) but vertical (scale-up) scalability as well. MBal is able to make efficient use of available resources in the cloud through its fine-grained, partitioned, lockless design. In the second part of this dissertation (Chapter 4 and Chapter5), we design and build CAST (Chapter 4), a Cloud Analytics Storage Tiering solution that cloud tenants can use to reduce monetary cost and improve performance of analytics workloads. The approach takes the first step towards providing storage tiering support for data analytics in the cloud. Furthermore, we propose a hybrid cloud object storage system (Chapter 5) that could effectively engage both the cloud service providers and cloud tenants via a novel dynamic pricing mechanism. In the third part of this dissertation (Chapter 6), targeting local storage, we explore offline algorithms for flash caching in terms of both hit ratio and flash lifespan. We design and implement a multi-stage heuristic by synthesizing several techniques that manage data at the granularity of a flash erasure unit (which we call a container) to approximate the offline optimal algorithm. In the fourth part of this dissertation (Chapter 7), we are focused on how to enable fast prototyping of efficient distributed key-value stores targeting a proxy-based layered architecture. In this work, we design and build {con}, a framework that significantly reduce the engineering effort required to build a full-fledged distributed key-value store. Our dissertation shows that simple workload-aware data management strategies can bring huge benefit in terms of both efficiency (i.e., performance, monetary cost, etc.) and flexibility (i.e., ease-of-use, ease-of-deployment, programmability, etc.). The principles of leveraging workload dynamicity and storage heterogeneity can be used to guide next-generation storage system software design, especially when being faced with new storage hardware technologies. / Ph. D.

Spatial database systems widely employ spatial indexing structures to speed up the processing of spatial queries. Many of the proposed spatial indices in the literature, such as the R-tree, assume magnetic disks (i.e., HDDs) as the underlying storage device. They are termed as disk-based spatial indices. On the other hand, several spatial database applications are increasingly using flash-based Solid State Drives (SSDs) and thus, designing spatial indices for these storage devices has gained increasing attention. This is due the fact that, compared to HDDs, SSDs offer smaller size, lighter weight, lower power consumption, better shock resistance, and faster reads and writes. Hence, specific indices for SSDs, termed flash-aware spatial indices, have been proposed in the literature to deal with the intrinsic characteristics of SSDs, such as the asymmetric costs of reads and writes. However, the research to date has not been able to establish a flash-aware spatial index that actually exploits all the benefits of SSDs. This PhD thesis advances on the literature as follows. We firstly define a methodology to create spatial datasets for experimental evaluations. We also propose FESTIval, a versatile framework that provides a common and unique environment to execute experimental evaluations. Such contributions served as a foundation to conduct performance analysis along this PhD work. By using this foundation, we analyze the performance behavior of spatial indices on different storage devices, such as HDDs and SSDs. Further, we discuss the applicability of employing flash simulators on the evaluation of spatial indices. The findings of these experiments contributed to the proposal of eFIND, a generic and efficient framework for flash-aware spatial indexing. eFIND is generic because it can port a wide range of disk-based spatial indices to SSDs. eFIND is also efficient because it is based on a set of design goals that exploits SSD performance. Performance tests showed that, compared to the state of the art, eFIND improved the construction of ported disk-based spatial indices and the execution of spatial queries. For porting the R-tree (i.e., the eFIND R-tree), eFIND showed performance reductions from 43% to 77% to build spatial indices, and from 4% to 23% to execute spatial queries. For porting the xBR+-tree (i.e., the eFIND xBR+-tree), eFIND showed reductions from 28% to 83% to build spatial indices and up to 35% in the spatial query processing. / Sistemas de banco de dados espaciais empregam estruturas de indexação espaciais para acelerar o processamento de consultas espaciais. Muitos dos índices espaciais propostos na literatura, como a R-tree, assumem que os dispositivos de armazenamentos são os discos magnéticos (i.e., HDDs) e são denominados índices espaciais baseados em disco. Por outro lado, várias aplicações de banco de dados espaciais estão cada vez mais usando Solid State Drives (SSDs) baseados em memória flash e, assim, projetar índices espaciais para esses dispositivos tem ganhado cada vez mais atenção. Isso se deve ao fato de que, em comparação com os HDDs, os SSDs oferecem menor tamanho, menor peso, menor consumo de energia, melhor resistência a choques além de leituras e escritas mais rápidas. Assim, índices espaciais para memória flash têm sido propostos na literatura para lidar com as características intrínsecas dos SSDs, como os custos assimétricos de leituras e escritas. No entanto, a pesquisa até o momento não conseguiu estabelecer um índice espacial que realmente explora todos os benefícios dos SSDs. Esta tese de doutorado avança na literatura da seguinte forma. Primeiramente, é definida uma metodologia para criar conjuntos de dados espaciais para avaliações experimentais. Também é proposto FESTIval, um arcabouço versátil que fornece um ambiente comum e único para executar avaliações experimentais. Tais contribuições serviram como base para conduzir análises de desempenho ao longo deste trabalho de doutorado. Usando essa base, o comportamento de desempenho de índices espaciais em diferentes dispositivos de armazenamento, como HDDs e SSDs, é analisado. Além disso, discutese a aplicabilidade de simuladores flash na avaliação experimental de índices espaciais. Os resultados desses experimentos contribuíram para a proposta de eFIND, uma estrutura genérica e eficiente para indexação espacial em memórias flash. eFIND é genérico porque pode portar uma ampla gama de índices espaciais baseados em disco para SSDs. eFIND também é eficiente porque é baseado em um conjunto de objetivos de projeto que exploram o desempenho do SSD. Os testes de desempenho mostraram que, em comparação com o estado da arte, eFIND melhorou a construção de índices espaciais portados e a execução de consultas espaciais. Para portar a R-tree (ou seja, a eFIND R-tree), eFIND mostrou melhorias de desempenho de 43% a 77% para construir índices espaciais e de 4% a 23% para executar consultas espaciais. Para portar a xBR+-tree (ou seja, a eFIND xBR+-tree), eFIND mostrou melhorias de 28% a 83% para construir índices espaciais e de até 35% no processamento de consultas espaciais.

Energy issues gathered strong attention over the past decade, reaching IT data processing infrastructures. Now, they need to cope with such responsibility, adjusting existing platforms to reach acceptable performance while promoting energy consumption reduction. As the de facto platform for Big Data, Apache Hadoop has evolved significantly over the last years, with more than 60 releases bringing new features. By implementing the MapReduce programming paradigm and leveraging HDFS, its distributed file system, Hadoop has become a reliable and fault tolerant middleware for parallel and distributed computing over large datasets. Nevertheless, Hadoop may struggle under certain workloads, resulting in poor performance and high energy consumption. Users increasingly demand that high performance computing solutions address sustainability and limit energy consumption. In this thesis, we introduce HDFSH, a hybrid storage mechanism for HDFS, which uses a combination of Hard Disks and Solid-State Disks to achieve higher performance while saving power in Hadoop computations. HDFSH brings, to the middleware, the best from HDs (affordable cost per GB and high storage capacity) and SSDs (high throughput and low energy consumption) in a configurable fashion, using dedicated storage zones for each storage device type. We implemented our mechanism as a block placement policy for HDFS, and assessed it over six recent releases of Hadoop with different architectural properties. Results indicate that our approach increases overall job performance while decreasing the energy consumption under most hybrid configurations evaluated. Our results also showed that, in many cases, storing only part of the data in SSDs results in significant energy savings and execution speedups / Ao longo da última década, questões energéticas atraíram forte atenção da sociedade, chegando às infraestruturas de TI para processamento de dados. Agora, essas infraestruturas devem se ajustar a essa responsabilidade, adequando plataformas existentes para alcançar desempenho aceitável enquanto promovem a redução no consumo de energia. Considerado um padrão para o processamento de Big Data, o Apache Hadoop tem evoluído significativamente ao longo dos últimos anos, com mais de 60 versões lançadas. Implementando o paradigma de programação MapReduce juntamente com o HDFS, seu sistema de arquivos distribuídos, o Hadoop tornou-se um middleware tolerante a falhas e confiável para a computação paralela e distribuída para grandes conjuntos de dados. No entanto, o Hadoop pode perder desempenho com determinadas cargas de trabalho, resultando em elevado consumo de energia. Cada vez mais, usuários exigem que a sustentabilidade e o consumo de energia controlado sejam parte intrínseca de soluções de computação de alto desempenho. Nesta tese, apresentamos o HDFSH, um sistema de armazenamento híbrido para o HDFS, que usa uma combinação de discos rígidos e discos de estado sólido para alcançar maior desempenho, promovendo economia de energia em aplicações usando Hadoop. O HDFSH traz ao middleware o melhor dos HDs (custo acessível por GB e grande capacidade de armazenamento) e SSDs (alto desempenho e baixo consumo de energia) de forma configurável, usando zonas de armazenamento dedicadas para cada dispositivo de armazenamento. Implementamos nosso mecanismo como uma política de alocação de blocos para o HDFS e o avaliamos em seis versões recentes do Hadoop com diferentes arquiteturas de software. Os resultados indicam que nossa abordagem aumenta o desempenho geral das aplicações, enquanto diminui o consumo de energia na maioria das configurações híbridas avaliadas. Os resultados também mostram que, em muitos casos, armazenar apenas uma parte dos dados em SSDs resulta em economia significativa de energia e aumento na velocidade de execução

Energy issues gathered strong attention over the past decade, reaching IT data processing infrastructures. Now, they need to cope with such responsibility, adjusting existing platforms to reach acceptable performance while promoting energy consumption reduction. As the de facto platform for Big Data, Apache Hadoop has evolved significantly over the last years, with more than 60 releases bringing new features. By implementing the MapReduce programming paradigm and leveraging HDFS, its distributed file system, Hadoop has become a reliable and fault tolerant middleware for parallel and distributed computing over large datasets. Nevertheless, Hadoop may struggle under certain workloads, resulting in poor performance and high energy consumption. Users increasingly demand that high performance computing solutions address sustainability and limit energy consumption. In this thesis, we introduce HDFSH, a hybrid storage mechanism for HDFS, which uses a combination of Hard Disks and Solid-State Disks to achieve higher performance while saving power in Hadoop computations. HDFSH brings, to the middleware, the best from HDs (affordable cost per GB and high storage capacity) and SSDs (high throughput and low energy consumption) in a configurable fashion, using dedicated storage zones for each storage device type. We implemented our mechanism as a block placement policy for HDFS, and assessed it over six recent releases of Hadoop with different architectural properties. Results indicate that our approach increases overall job performance while decreasing the energy consumption under most hybrid configurations evaluated. Our results also showed that, in many cases, storing only part of the data in SSDs results in significant energy savings and execution speedups / Ao longo da última década, questões energéticas atraíram forte atenção da sociedade, chegando às infraestruturas de TI para processamento de dados. Agora, essas infraestruturas devem se ajustar a essa responsabilidade, adequando plataformas existentes para alcançar desempenho aceitável enquanto promovem a redução no consumo de energia. Considerado um padrão para o processamento de Big Data, o Apache Hadoop tem evoluído significativamente ao longo dos últimos anos, com mais de 60 versões lançadas. Implementando o paradigma de programação MapReduce juntamente com o HDFS, seu sistema de arquivos distribuídos, o Hadoop tornou-se um middleware tolerante a falhas e confiável para a computação paralela e distribuída para grandes conjuntos de dados. No entanto, o Hadoop pode perder desempenho com determinadas cargas de trabalho, resultando em elevado consumo de energia. Cada vez mais, usuários exigem que a sustentabilidade e o consumo de energia controlado sejam parte intrínseca de soluções de computação de alto desempenho. Nesta tese, apresentamos o HDFSH, um sistema de armazenamento híbrido para o HDFS, que usa uma combinação de discos rígidos e discos de estado sólido para alcançar maior desempenho, promovendo economia de energia em aplicações usando Hadoop. O HDFSH traz ao middleware o melhor dos HDs (custo acessível por GB e grande capacidade de armazenamento) e SSDs (alto desempenho e baixo consumo de energia) de forma configurável, usando zonas de armazenamento dedicadas para cada dispositivo de armazenamento. Implementamos nosso mecanismo como uma política de alocação de blocos para o HDFS e o avaliamos em seis versões recentes do Hadoop com diferentes arquiteturas de software. Os resultados indicam que nossa abordagem aumenta o desempenho geral das aplicações, enquanto diminui o consumo de energia na maioria das configurações híbridas avaliadas. Os resultados também mostram que, em muitos casos, armazenar apenas uma parte dos dados em SSDs resulta em economia significativa de energia e aumento na velocidade de execução

<p dir="ltr">The goal of the study was to investigate vowel accuracy in preschoolers with and without speech sound disorders (SSD) on nonword repetition tasks. Children ages 4 and 5 years completed a battery of speech and language measures, as well as two nonword repetition tasks, the Syllable Repetition Task (SRT) and the Nonword Repetition Task (NRT). A total of 41 children participated in the study divided among 3 groups: 14 children with typical speech and language skills (TD), 14 children with isolated speech sound disorders (SSD-only), and 13 children with SSD and comorbid Developmental Language Disorder (SSD+DLD). Ultimately, it appears that children with SSD+DLD make more vowel errors on the Nonword Repetition Task, and that vowel accuracy on nonword repetition tasks and phonological awareness may share underlying mechanisms.</p>

Philosophiae Doctor - PhD / The Student Support and Development Services (SSDS) at Further Education and Training (FET) colleges represent a holistic and systemic approach to addressing barriers to learning and development. College SSDS are based on the acknowledgement that all FET students need support and development and that, when addressing needs of the college student, it is done in a holistic, integrated, intersectional and inclusive manner. The SSDS therefore strive to develop competencies, knowledge, skills and attitudes in a systemic and holistic manner. The aim of the study was to design, implement and evaluate SSDS at the FET colleges in the Western Cape Education ..Department and to establish a Lecturer Support and Development Team (LSDT) at each FET college, which would include the following services: • student counselling services • academic development and learning support • occupational development and career guidance • life skills education and health education, and • college institutional development. Student counselling services at FET colleges aim to render comprehensive student services with a holistic developmental aspect of the student in relation to his/her social, emotional, physical and cognitive dimensions. The staff of the LSDT are the first line of contact for the troubled student. The aim of the study was to design, implement and evaluate SSDS at the FET colleges in the Western Cape Education ..Department and to establish a Lecturer Support and Development Team (LSDT) at each FET college, which would include the following services: • student counselling services • academic development and learning support • occupational development and career guidance • life skills education and health education, and • college institutional development. Academic development is aimed at the students who enter the FET sector with inadequate schooling, education and training. Orientation programmes include bridging the gap between schooling and FET education and training. Bridging programmes and remedial programmes are offered to students to compensate for their academic backlog and to accelerate their education and training up to a level suitable for FET. Within the context of academic development learning support is targeted at students experiencing learning and training barriers. This should occur within the framework of inclusive education and training at FET colleges, which is in acknowledgement of the belief that all students can learn and be trained at FET colleges. The development and support mechanisms at the college aim to offer comprehensive assistance to students who experience barriers to education and training. Occupational development and career guidance are aimed at helping the students to make informed and meaningful subject and course decisions which will enable them to enter a suitable occupation in the world of work. Occupational development is the development of appropriate skills, knowledge and competencies to keep students abreast with the fast and ever changing demands of the world of commerce and industry. The South African Qualifications Authority (SAQA) development has introduced student learnerships as a 'way of equipping students with the appropriate practical skills for a career. Life skills and health education includes the development of the well-being of students along physical, psychological, social and cognitive dimensions. The HIV/AIDS strategies are the highest priority at the FET colleges. The health promoting education and preventative measures are programmes aimed at minimising risks and giving the student coping strategies. Life skills and health education, which includes sexuality, are infused in the classroom education and training curriculum to ensure that all students are exposed to indispensable information aimed at equipping them for life in the world of work. College institutional development is the ongoing development of the college organisation where the college staff, management and community develop a systemic way of thinking about and managing change. With the clustering and amalgamation of colleges in South Africa, the transformation of the FET colleges necessitates adaptation of the individual and group systems to the development of education and training. The research commenced with a pilot study at eight technical colleges of the Western Cape Education Department (WCED). The study followed a quantitative and qualitative research method to collect data from the college, staff and students. Two questionnaires were used to do a needs analysis. The College Institution Evaluation Questionnaire was then administered to 58 colleges in South Africa and the College Student Profile questionnaire to 2175 students. The data from these questionnaires were used to construct the training programme for the student counselling, academic development and learning support, occupational development and career guidance, life skills and health education programmes which were being conducted at 18 colleges of the WCED. A Student Support and Development Evaluation Questionnaire was then used to evaluate the effectiveness and value of the SSDS programmes. During an interview the Lecture Support and Development Interviewing Schedule was used to evaluate and to determine the efficiency of the LSDT at the college.