From Controlled Data-Center Environments to Open Distributed Environments: Scalable, Efficient, and Robust Systems with Extended Functionality

The past two decades have witnessed several paradigm shifts in computing environments. Starting from cloud computing which offers on-demand allocation of storage, network, compute, and memory resources, as well as other services, in a pay-as-you-go billingmodel. Ending with the rise of permissionless blockchain technology, a decentralized computing paradigm with lower trust assumptions and limitless number of participants. Unlike in the cloud, where all the computing resources are owned by some trusted cloud provider, permissionless blockchains allow computing resources owned by possibly malicious parties to join and leave their network without obtaining permission from some centralized trusted authority. Still, in the presence of malicious parties, permissionlessblockchain networks can perform general computations and make progress. Cloud computing is powered by geographically distributed data-centers controlled and managed by trusted cloud service providers and promises theoretically infinite computing resources. On the other hand, permissionless blockchains are powered by open networks of geographically distributed computing nodes owned by entities that are not necessarily known or trusted. This paradigm shift requires a reconsideration of distributed data management protocols and distributed system designs that assume low latency across system components, inelastic computing resources, or fully trusted computing resources.In this dissertation, we propose new system designs and optimizations that address scalability and efficiency of distributed data management systems in cloud environments. We also propose several protocols and new programming paradigms to extend the functionality and enhance the robustness of permissionless blockchains. The work presented spans global-scale transaction processing, large-scale stream processing, atomic transaction processing across permissionless blockchains, and extending the functionality and the use-cases of permissionless blockchains. In all these directions, the focus is on rethinking system and protocol designs to account for novel cloud and permissionless blockchain assumptions. For global-scale transaction processing, we propose GPlacer, a placement optimization framework that decides replica placement of fully and partial geo-replicated databases. For large-scale stream processing, we propose Cache-on-Track (CoT) an adaptive and elastic client-side cache that addresses server-side load-imbalances that occur in large-scale distributed storage layers. In permissionless blockchain transaction processing, we propose AC3WN, the first correct cross-chain commitment protocol that guarantees atomicity of cross-chain transactions. Also, we propose TXSC, a transactional smart contract programming framework. TXSC provides smart contract developers with transaction primitives. These primitives allow developers to write smart contracts without the need to reason about the anomalies that can arise due to concurrent smart contract function executions. In addition, we propose a forward-looking architecture that unifies both permissioned and permissionless blockchains and exploits the running infrastructure of permissionless blockchains to build global asset management systems

Entrega de conteúdos multimédia em over-the-top: caso de estudo das gravações automáticas

Doutoramento em Engenharia EletrotécnicaOver-The-Top (OTT) multimedia delivery is a very appealing approach for providing ubiquitous, exible, and globally accessible services capable of low-cost and unrestrained device targeting. In spite of its appeal, the underlying delivery architecture must be carefully planned and optimized to maintain a high Qualityof- Experience (QoE) and rational resource usage, especially when migrating from services running on managed networks with established quality guarantees. To address the lack of holistic research works on OTT multimedia delivery systems, this Thesis focuses on an end-to-end optimization challenge, considering a migration use-case of a popular Catch-up TV service from managed IP Television (IPTV) networks to OTT. A global study is conducted on the importance of Catch-up TV and its impact in today's society, demonstrating the growing popularity of this time-shift service, its relevance in the multimedia landscape, and tness as an OTT migration use-case. Catch-up TV consumption logs are obtained from a Pay-TV operator's live production IPTV service containing over 1 million subscribers to characterize demand and extract insights from service utilization at a scale and scope not yet addressed in the literature. This characterization is used to build demand forecasting models relying on machine learning techniques to enable static and dynamic optimization of OTT multimedia delivery solutions, which are able to produce accurate bandwidth and storage requirements' forecasts, and may be used to achieve considerable power and cost savings whilst maintaining a high QoE. A novel caching algorithm, Most Popularly Used (MPU), is proposed, implemented, and shown to outperform established caching algorithms in both simulation and experimental scenarios. The need for accurate QoE measurements in OTT scenarios supporting HTTP Adaptive Streaming (HAS) motivates the creation of a new QoE model capable of taking into account the impact of key HAS aspects. By addressing the complete content delivery pipeline in the envisioned content-aware OTT Content Delivery Network (CDN), this Thesis demonstrates that signi cant improvements are possible in next-generation multimedia delivery solutions.A entrega de conteúdos multimédia em Over-The-Top (OTT) e uma proposta atractiva para fornecer um serviço flexível e globalmente acessível, capaz de alcançar qualquer dispositivo, com uma promessa de baixos custos. Apesar das suas vantagens, e necessario um planeamento arquitectural detalhado e optimizado para manter níveis elevados de Qualidade de Experiência (QoE), em particular aquando da migração dos serviços suportados em redes geridas com garantias de qualidade pré-estabelecidas. Para colmatar a falta de trabalhos de investigação na área de sistemas de entrega de conteúdos multimédia em OTT, esta Tese foca-se na optimização destas soluções como um todo, partindo do caso de uso de migração de um serviço popular de Gravações Automáticas suportado em redes de Televisão sobre IP (IPTV) geridas, para um cenário de entrega em OTT. Um estudo global para aferir a importância das Gravações Automáticas revela a sua relevância no panorama de serviços multimédia e a sua adequação enquanto caso de uso de migração para cenários OTT. São obtidos registos de consumos de um serviço de produção de Gravações Automáticas, representando mais de 1 milhão de assinantes, para caracterizar e extrair informação de consumos numa escala e âmbito não contemplados ate a data na literatura. Esta caracterização e utilizada para construir modelos de previsão de carga, tirando partido de sistemas de machine learning, que permitem optimizações estáticas e dinâmicas dos sistemas de entrega de conteúdos em OTT através de previsões das necessidades de largura de banda e armazenamento, potenciando ganhos significativos em consumo energético e custos. Um novo mecanismo de caching, Most Popularly Used (MPU), demonstra um desempenho superior as soluções de referencia, quer em cenários de simulação quer experimentais. A necessidade de medição exacta da QoE em streaming adaptativo HTTP motiva a criaçao de um modelo capaz de endereçar aspectos específicos destas tecnologias adaptativas. Ao endereçar a cadeia completa de entrega através de uma arquitectura consciente dos seus conteúdos, esta Tese demonstra que são possíveis melhorias de desempenho muito significativas nas redes de entregas de conteúdos em OTT de próxima geração

The use of maize streak virus (MSV) replication-associated protein mutants in the development of MSV-resistant plants

Bibliography: pages 170-194.Maize streak virus (MSV) is the type member of the Mastrevirus genus of the Geminiviridae. As the causal agent of maize streak disease (MSD), MSV is the most significant pathogen of maize in Africa, resulting in crop yield losses of up to 100%. Transmitted by leafhoppers (Cicadulina spp.), MSV is indigenous to Africa and neighbouring Indian Ocean Islands. Despite maize being a crucial staple food crop in Africa, the average maize yield per hectare in Africa is the lowest in the world, leading to food shortages and famine. A major contributing factor to these low yields is MSD. To genetically engineer MSV-resistant maize using the pathogen-derived resistance (PDR) strategy, the viral replication-associated (Rep) protein gene was targeted, whose multifunctional products Rep and RepA are the only viral proteins essential for replication. Rep constructs had previously been made containing deleterious mutations in several conserved amino acid motifs. In this study, these mutants and the wild type Rep gene were truncated to remove key motifs involved in viral replication. A quantitative PCR assay was developed to determine the effects of the mutant and truncated Reps on viral replication in black Mexican sweetcorn (BMS) suspension cells. The MSVsensitive grass Digitaria sanguinalis was then transformed with Rep constructs that inhibited MSV replication in BMS, and transgenic lines were tested for virus resistance. Several plants of a D. sanguinalis line transgenic for a mutated full-length Rep gene showed excellent resistance (immunity) to MSV, but the transgene had negative effects on aspects of plant growth and development. Transformation with a mutated/truncated Rep gene resulted in healthy fertile transgenic D. sanguinalis plants, many of which showed good MSV resistance. Fertile maize (Hi-II) T 1 transgenic plants expressing the truncated/mutated Rep gene have been obtained, the offspring of which will be tested for resistance to MSV. Considering the success in achieving MSV-resistant D. sanguinalis, there is good reason to believe that the transgenic maize will too be resistant to MSV

Efficient data reconfiguration for today's cloud systems

Performance of big data systems largely relies on efficient data reconfiguration techniques. Data reconfiguration operations deal with changing configuration parameters that affect data layout in a system. They could be user-initiated like changing shard key, block size in NoSQL databases, or system-initiated like changing replication in distributed interactive analytics engine. Current data reconfiguration schemes are heuristics at best and often do not scale well as data volume grows. As a result, system performance suffers. In this thesis, we show that {\it data reconfiguration mechanisms can be done in the background by using new optimal or near-optimal algorithms coupling them with performant system designs}. We explore four different data reconfiguration operations affecting three popular types of systems -- storage, real-time analytics and batch analytics. In NoSQL databases (storage), we explore new strategies for changing table-level configuration and for compaction as they improve read/write latencies. In distributed interactive analytics engines, a good replication algorithm can save costs by judiciously using memory that is sufficient to provide the highest throughput and low latency for queries. Finally, in batch processing systems, we explore prefetching and caching strategies that can improve the number of production jobs meeting their SLOs. All these operations happen in the background without affecting the fast path. Our contributions in each of the problems are two-fold -- 1) we model the problem and design algorithms inspired from well-known theoretical abstractions, 2) we design and build a system on top of popular open source systems used in companies today. Finally, using real-life workloads, we evaluate the efficacy of our solutions. Morphus and Parqua provide several 9s of availability while changing table level configuration parameters in databases. By halving memory usage in distributed interactive analytics engine, Getafix reduces cost of deploying the system by 10 million dollars annually and improves query throughput. We are the first to model the problem of compaction and provide formal bounds on their runtime. Finally, NetCachier helps 30\% more production jobs to meet their SLOs compared to existing state-of-the-art

STR profile authentication via machine learning techniques

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 169-171).Short tandem repeat (STR) DNA profiles have multiple uses in forensic analysis, kinship identification, and human biometrics. However, as biotechnology progresses, there is a growing concern that STR profiles can be created using standard laboratory techniques such as whole genome amplification and molecular cloning. Such technologies can be used to synthesize any STR profile without the need for a physical sample, only knowledge of the desired genetic sequence. Therefore, to preserve the credibility of DNA as a forensic tool, it is imperative to develop means to authenticate STR profiles. The leading technique in the field, methylation analysis, is accurate but also expensive, time-consuming, and degrades the forensic sample so that further analysis is not possible. The realm of machine learning offers techniques to address the need for more effective STR profile authentication. In this work, a set of features were identified at both the channel and profile levels of STR electropherograms. A number of supervised and unsupervised machine learning algorithms were then used to predict whether a given STR electropherogram was authentic or synthesized by laboratory techniques. With the aid of the LNKnet machine learning toolkit, various classifiers were trained with the default set of parameters and the full set of features to quantify their baseline performance. Particular emphasis was placed on detecting profiles generated by Whole Genome Amplification (WGA). A greedy forward-backward search algorithm was implemented to determine the most useful subset of features from the initial group. Though the set of optimal feature values varied by classifier, a trend was observed indicating that the inter-locus imbalance error, stutter count, and range of peak widths for a profile were particularly useful features. These were selected by over two thirds of the classifiers. The signal-to- noise ratio was also a useful feature, selected by seven out of 16 classifiers. The selected features were in turn used to tune the parameters of machine learning algorithms and to compare their performance. From a set of 16 initial classifiers, the K-nearest neighbors, condensed K-nearest neighbors, multi-layer perceptron, Parzen window, and support vector machine classifiers achieved the best performance. These classification algorithms all attained error rates of approximately ten percent, defined as the percentage of profiles misclassified with the highest performing classifier achieving an error rate of less than eight percent. Overall, the classifiers performed well at detecting artificial profiles but had more difficulty accurately distinguishing natural profiles. There were many false positives for the artificial class, since profiles in this category took on a greater range of feature values. Finally, preliminary steps were taken to form classifier committees. However, combining the top performing classifiers via a majority vote did not significantly improve performance. The results of this work demonstrate the feasibility of a completely software-based approach to profile authentication. They confirm that machine learning techniques are a useful tool to trigger further investigation of profile authenticity via more expensive approaches.by Anna Shcherbina.M.Eng

General Catalogue 1949

General Catalogue of 1949 Contains course descriptions, University college calendar, and college administration.https://digitalcommons.usu.edu/universitycatalogs/1076/thumbnail.jp

Southern Accent September 1984 - April 1985

Southern Adventist University\u27s newspaper, Southern Accent, for the academic year of 1984-1985.https://knowledge.e.southern.edu/southern_accent/1060/thumbnail.jp