Context: Cloud computing is providing services and resources to customers based on pay-per-use. As the services increasing, Cloud computing using a vast number of data centers like thousands of data centers which consumes high energy. The power consumption for cooling the data centers is very high. So, recent research going on to implement the best model to reduce the energy consumption by the data centers. This process of minimizing the energy consumption can be done using dynamic Virtual Machine Consolidation (VM Consolidation) in which there will be a migration of VMs from one host to another host so that energy can be saved. 70% of energy consumption will be reduced/ saved when the host idle mode is switched to sleep mode, and this is done by migration of VM from one host to another host. There are many energy adaptive heuristics algorithms for the VM Consolidation. Host overload detection, host underload detection and VM selection using VM placement are the heuristics algorithms of VM Consolidation which results in less consumption of the energy in the data centers while meeting Quality of Service (QoS). In this thesis, we proposed new heuristic algorithms to reduce energy consumption. Objectives: The objective of this research is to provide an energy efficient model to reduce energy consumption. And proposing a new heuristics algorithms of VM Consolidationtechnique in such a way that it consumes less energy. Presenting the advantages and disadvantages of the proposed heuristics algorithms is also considered as objectives of our experiment. Methods: Literature review was performed to gain knowledge about the working and performances of existing algorithms using VM Consolidation technique. Later, we have proposed a new host overload detection, host underload detection, VM selection, and VM placement heuristic algorithms. In our work, we got 32 combinations from the host overload detection and VM selection, and two VM placement heuristic algorithms. We proposed dynamic host underload detection algorithm which is used for all the 32 combinations. The other research method chosen is experimentation, to analyze the performances of both proposed and existing algorithms using workload traces of PlanetLab. This simulation is done usingCloudSim. Results: To compare and get the results, the following parameters had been considered: Energy consumption, No. of migrations, Performance Degradation due to VM Migrations (PDM),Service Level Agreement violation Time per Active Host (SLATAH), SLA Violation (SLAV),i.e. from a combination of the PDM, SLATAH, Energy consumption and SLA Violation (ESV).We have conducted T-test and Cohen’s d effect size to measure the significant difference and effect size between algorithms respectively. For analyzing the performance, the results obtained from proposed algorithms and existing algorithm were compared. From the 32 combinations of the host overload detection and VM Selection heuristic algorithms, MADmedian_MaxR (Mean Absolute Deviation around median (MADmedian) and Maximum Requested RAM (MaxR))using Modified Worst Fit Decreasing (MWFD) VM Placement algorithm, andMADmean_MaxR (Mean Absolute Deviation around mean (MADmean), and MaximumRequested RAM (MaxR)) using Modified Second Worst Fit Decreasing (MSWFD) VM placement algorithm respectively gives the best results which consume less energy and with minimum SLA Violation. Conclusion: By analyzing the comparisons, it is concluded that proposed algorithms perform better than the existing algorithm. As our aim is to propose the better energy- efficient model using the VM Consolidation techniques to minimize the power consumption while meeting the SLAs. Hence, we proposed the energy- efficient algorithms for VM Consolidation technique and compared with the existing algorithm and proved that our proposed algorithm performs better than the other algorithm. We proposed 32 combinations of heuristics algorithms (host overload detection and VM selection) with two adaptive heuristic VM placement algorithms. We have proposed a dynamic host underload detection algorithm, and it is used for all 32 combinations. When the proposed algorithms are compared with the existing algorithm, we got 22 combinations of host overload detection and VM Selection heuristic algorithms with MWFD(Modified Worst Fit Decreasing) VM placement and 20 combinations of host overload detection and VM Selection heuristic algorithms with MSWFD (Modified Second Worst FitDecreasing) VM placement algorithm which shows the better performance than existing algorithm. Thus, our proposed heuristic algorithms give better results with minimum energy consumption with less SLA violation.

This thesis reports on the research undertaken to reduce the water usage within manufacturing through modelling and improving the water efficiencies at both process and production system levels. The primary objectives of this research are: to develop a framework which classifies the various water usages within a manufacturing facility, to define a number of efficiency ratios to highlight the water inefficient activities, and to develop a decision support tool to aid with the selection of the most effective solutions for reduction of water usage within manufacturing applications. The research undertaken in the past three years is divided into four main parts. The first part reviews the relevant literature on water availability and distribution, the role of water in manufacturing, and relevant legislations and policies governing the water usage in manufacturing industries. The review also includes assessment of current water flow modelling and wastewater management tools and technologies. The second part introduces a Manufacturing Water Usage framework which classifies the water usage within a manufacturing facility as production-related and non-production-related water. The Production Water, which is the main focus of this research, is further classified as Process and System Water required to produce a product. Process Water is defined as the water used directly by the production processes, whereas System Water is defined as the water used to support the operation of a process and/or to maintain the production equipment. The framework also incorporates the definition of a number of Water Efficiency Ratios to determine the water critical processes. The third part of the thesis describes the implementation of this framework within a water simulation model as the main engine for a water reduction decision support tool. The final part of the thesis demonstrates the utilisation of this tool to support the decisions aimed at reducing water within a real food production line. In summary, the research has concluded that the consideration of water reduction scenarios within the manufacturing industry requires a detailed understanding of where and how water is used at production process level, and utilisation of this knowledge to develop a series of proactive approaches based on product/process redesign and radical operational planning improvements.

Cooperative diversity exploits the broadcast nature of wireless channels and uses relays to improve link reliability. Most cooperative communication protocols are assumed to be synchronous in nature, which is not always possible in wireless communication. Also the relay nodes are assumed to be half duplex which in turn reduces the spectral efficiency. In this thesis, we first present a novel asynchronous cooperative communication protocol exploiting polarization diversity, which does not require synchronization at the relay node. Dual polarized antennas are employed at the relay node to achieve full duplex amplify-and-forward (ANF) communication. Hence the transmission duration is reduced which results into an increased throughput rate. Capacity analysis of the proposed scheme ascertains the high data rate as compared to conventional ANF. Bit error rate (BER) simulation also shows that the proposed scheme significantly outperforms both the non-cooperative single-input single-output and the conventional ANF schemes. Considering channel path loss, the proposed scheme consume less total transmission energy as compared to ANF and non-cooperative scheme in more practical distance range. Thus the proposed scheme is suitable for high rate and energy efficient relay-enabled communication. In addition to that, we also present a novel power allocation scheme for multiple relay nodes that results in efficient cooperative multiple-input multiple-output (MIMO) communication. Considering channel path loss, the total transmission energy is distributed between the source and the relay nodes. The energy distribution ratio between the relay and direct link is optimized such that the quality of received signal is maintained with minimum total transmission energy consumption. We calculate the energy distribution ratio analytically and verified it through computer simulation. With the new power allocation scheme, the system also obtains an increased channel capacity as compared to cooperative scheme with conventional equal power allocation and non-cooperative scheme. Optimal relay positioning with proposed energy allocation scheme is also explored to maximize the capacity.

Submitted by Emanuel Varela Cardoso (emanuel.varela@ufcg.edu.br) on 2018-09-11T19:44:07Z No. of bitstreams: 1 DEMETRIO GOMES MESTRE – TESE (PPGCC) 2018.pdf: 15362740 bytes, checksum: eb531a72836b3c7f2f4e0171c7f563dc (MD5) / Made available in DSpace on 2018-09-11T19:44:07Z (GMT). No. of bitstreams: 1 DEMETRIO GOMES MESTRE – TESE (PPGCC) 2018.pdf: 15362740 bytes, checksum: eb531a72836b3c7f2f4e0171c7f563dc (MD5) Previous issue date: 2018-03-27 / Entity Matching (EM), ou seja, a tarefa de identificar entidades que se referem a um mesmo objeto do mundo real, é uma tarefa importante e difícil para a integração e limpeza de fontes de dados. Uma das maiores dificuldades para a realização desta tarefa, na era de Big Data, é o tempo de execução elevado gerado pela natureza quadrática da execução da tarefa. Para minimizar a carga de trabalho preservando a qualidade na detecção de entidades similares, tanto para uma ou mais fontes de dados, foram propostos os chamados métodos de indexação ou blocagem. Estes métodos particionam o conjunto de dados em subconjuntos (blocos) de entidades potencialmente similares, rotulando-as com chaves de bloco, e restringem a execução da tarefa de EM entre entidades pertencentes ao mesmo bloco. Apesar de promover uma diminuição considerável no número de comparações realizadas, os métodos de indexação ainda podem gerar grandes quantidades de comparações, dependendo do tamanho dos conjuntos de dados envolvidos e/ou do número de entidades por índice (ou bloco). Assim, para reduzir ainda mais o tempo de execução, a tarefa de EM pode ser realizada em paralelo com o uso de modelos de programação tais como MapReduce e Spark. Contudo, a eficácia e a escalabilidade de abordagens baseadas nestes modelos depende fortemente da designação de dados feita da fase de map para a fase de reduce, para o caso de MapReduce, e da designação de dados entre as operações de transformação, para o caso de Spark. A robustez da estratégia de designação de dados é crucial para se alcançar alta eficiência, ou seja, otimização na manipulação de dados enviesados (conjuntos de dados grandes que podem causar gargalos de memória) e no balanceamento da distribuição da carga de trabalho entre os nós da infraestrutura distribuída. Assim, considerando que a investigação de abordagens que promovam a execução eficiente, em modo batch ou tempo real, de métodos de indexação adaptativa de EM no contexto da computação distribuída ainda não foi contemplada na literatura, este trabalho consiste em propor um conjunto de abordagens capaz de executar a indexação adaptativas de EM de forma eficiente, em modo batch ou tempo real, utilizando os modelos programáticos MapReduce e Spark. O desempenho das abordagens propostas é analisado em relação ao estado da arte utilizando infraestruturas de cluster e fontes de dados reais. Os resultados mostram que as abordagens propostas neste trabalho apresentam padrões que evidenciam o aumento significativo de desempenho da tarefa de EM distribuída promovendo, assim, uma redução no tempo de execução total e a preservação da qualidade da detecção de pares de entidades similares. / Entity Matching (EM), i.e., the task of identifying all entities referring to the same realworld object, is an important and difficult task for data sources integration and cleansing. A major difficulty for this task performance, in the Big Data era, is the quadratic nature of the task execution. To minimize the workload and still maintain high levels of matching quality, for both single or multiple data sources, the indexing (blocking) methods were proposed. Such methods work by partitioning the input data into blocks of similar entities, according to an entity attribute, or a combination of them, commonly called “blocking key”, and restricting the EM process to entities that share the same blocking key (i.e., belong to the same block). In spite to promote a considerable decrease in the number of comparisons executed, indexing methods can still generate large amounts of comparisons, depending on the size of the data sources involved and/or the number of entities per index (or block). Thus, to further minimize the execution time, the EM task can be performed in parallel using programming models such as MapReduce and Spark. However, the effectiveness and scalability of MapReduce and Spark-based implementations for data-intensive tasks depend on the data assignment made from map to reduce tasks, in the case of MapReduce, and the data assignment between the transformation operations, in the case of Spark. The robustness of this assignment strategy is crucial to achieve skewed data handling (large sets of data can cause memory bottlenecks) and balanced workload distribution among all nodes of the distributed infrastructure. Thus, considering that studies about approaches that perform the efficient execution of adaptive indexing EM methods, in batch or real-time modes, in the context of parallel computing are an open gap according to the literature, this work proposes a set of parallel approaches capable of performing efficient adaptive indexing EM approaches using MapReduce and Spark in batch or real-time modes. The proposed approaches are compared to state-of-the-art ones in terms of performance using real cluster infrastructures and data sources. The results carried so far show evidences that the performance of the proposed approaches is significantly increased, enabling a decrease in the overall runtime while preserving the quality of similar entities detection.

Orientador: José Alexandre Matelli / coorientador: José Antonio Perrella Balestieri / Banca: Rubens Alves Dias / Banca: Paloma Maria Silva Rocha Rizol / Banca: Jonny Carlos da Silva / Banca: Elisangela Martins Leal / Resumo: Este trabalho apresenta o desenvolvimento de um protótipo de sistema especialista (SE) dotado de um módulo de geração de explicação (MGE) para diagnóstico preliminar de eficiência energética em plantas industriais, considerando as características fundamentais de operação e manutenção dessas plantas. Embora a capacidade de explicar processos de raciocínio distinga o SE de outros sistemas de apoio à decisão, disponibilizar um MGE ajuda a promover uma análise crítica das decisões tomadas pelo SE. Em adição, o MGE é muito útil também na validação da base de conhecimento, permitindo detectar erros semânticos do sistema e monitorar detalhes do processo de inferência, o que pode levar a uma redução de tempo de desenvolvimento e maior aceitação do sistema, aumentando dessa forma sua confiabilidade e principalmente, sua credibilidade. A base de conhecimento do sistema foi construída a partir da experiência de especialistas multidisciplinares da área de eficiência energética industrial, o que é certamente um recurso valioso em um contexto de gestão do conhecimento (GC) organizacional. A GC torna o conhecimento humano especializado em valor duradouro, criando uma memória corporativa por meio de ações de compartilhamento e retenção, o que pode contribuir para a melhoria contínua dos processos, criação de valor e manutenção de vantagens competitivas. Para demonstrar as possibilidades de melhoria de eficiência energética, os principais sistemas de utilidades de uma planta industrial são ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This work presents the development of an expert system prototype (ES) with an explanation facility module (EFM) for preliminary diagnosis of energy efficiency guidelines in industrial plants, considering the fundamental characteristics of operation and maintenance of these plants. Although the ability to explain its own reasoning distinguishes ES from other decision support systems, providing an EFM contributes to promote a critical analysis of the decisions taken by the ES. In addition, the EFM is also very useful in validating the knowledge base, because it facilitates the detection of semantic errors and allows a better monitoring of the inference process. Because of that, the EFM can reduce the development time and contribute to a broader acceptance of the system, which positively impacts its reliability and credibility. The knowledge base of the system was built on the experience of multidisciplinary experts in the field of industrial energy efficiency that is certainly a valuable resource in an organizational knowledge management (KM) context. The KM becomes the human knowledge in permanent value by creating a corporate memory through sharing and retention actions that may contribute to continuous improvement of processes, value creation and maintaining competitive advantage. In order to properly demonstrate the possibilities of energy efficiency improvement, the main utility systems of an industrial plant are analyzed individually. To do so, such systems are classified... (Complete abstract click electronic access below) / Doutor

GARCIA, Fernando Parente. EPMOSt: um sistema de monitoramento passivo energeticamente eficiente para redes de sensores sem fio. 2014. 115 f. Tese (Doutorado em ciência da computação)- Universidade Federal do Ceará, Fortaleza-CE, 2014. / Submitted by Elineudson Ribeiro (elineudsonr@gmail.com) on 2016-07-19T19:35:08Z No. of bitstreams: 1 2014_tese_fpgarcia.pdf: 3632932 bytes, checksum: c2ab75ac7a41921760c110a2d98e4c5b (MD5) / Approved for entry into archive by Rocilda Sales (rocilda@ufc.br) on 2016-07-25T11:41:57Z (GMT) No. of bitstreams: 1 2014_tese_fpgarcia.pdf: 3632932 bytes, checksum: c2ab75ac7a41921760c110a2d98e4c5b (MD5) / Made available in DSpace on 2016-07-25T11:41:57Z (GMT). No. of bitstreams: 1 2014_tese_fpgarcia.pdf: 3632932 bytes, checksum: c2ab75ac7a41921760c110a2d98e4c5b (MD5) Previous issue date: 2014 / Monitoring systems are important for debugging and analyzing Wireless Sensor Networks (WSN). In passive monitoring, a monitoring network needs to be deployed in addition to the network to be monitored, called target network. The monitoring network captures and analyzes packets sent by the target network. An energy-efficient passive monitoring system is necessary when there is a need to monitor a WSN in a real scenario because the lifetime of the monitoring network is extended and, consequently, the target network benefits from the monitoring for a longer time. In this thesis, initially, the main passive monitoring systems proposed for WSN have been identified, analyzed and compared. During the literature review, no passive monitoring system for WSN that aims to reduce the energy consumption of the monitoring network has been identified. Therefore, this thesis proposes an Energy-efficient Passive MOnitoring System for WSN (EPMOSt) that extends the lifetime of the monitoring network. EPMOSt uses two mechanisms to reduce the energy consumption of the monitoring network: sniffer election and aggregation of headers. By using the sniffer election, in general only one sniffer (a node of the monitoring network) captures packets sent by a given node of the target network, thereby reducing the transmission of packets captured by the monitoring network and, thus, considerably reducing the energy consumption of this network. By using aggregation of headers, only the information present in the headers of captured packets is sent through the monitoring network. Thus, the headers of several packets may be sent in the same monitoring message, hence reducing the overhead of transmission and consequently reducing the energy consumption of the monitoring network. Experiments performed with real sensors and with a WSN simulator in various scenarios are conducted to evaluate the proposed monitoring system. The obtained results show the energy efficiency of the EPMOSt and the viability of using it to monitor WSN in real scenarios. / Sistemas de monitoramento permitem depurar e analisar o funcionamento de uma Rede de Sensores Sem Fio (RSSF). No monitoramento passivo, uma rede de monitoramento adicional é implantada com o intuito de capturar e analisar os pacotes transmitidos pela rede a ser monitorada, denominada rede alvo. Quando se deseja monitorar uma RSSF em um ambiente real, um sistema de monitoramento passivo energeticamente eficiente é necessário, pois, caso contrário, a rede de monitoramento pode ter um tempo de vida bem menor do que a rede alvo. Nesta tese, inicialmente, os principais sistemas de monitoramento passivo propostos para RSSF foram identificados, analisados e comparados. Durante as pesquisas realizadas na literatura, não foi identificado nenhum sistema de monitoramento passivo que se preocupasse em reduzir o consumo de energia da rede de monitoramento. Sendo assim, esta tese propõe um sistema de monitoramento passivo energeticamente eficiente para RSSF, denominado EPMOSt (Energy-efficient Passive MOnitoring System), que prolonga o tempo de vida da rede de monitoramento. O EPMOSt utiliza dois mecanismos para reduzir o consumo de energia da rede de monitoramento: eleição de sniffers (nós da rede de monitoramento) e agregação de cabeçalhos. A eleição de sniffers garante que durante a maior parte do tempo apenas um sniffer captura os pacotes transmitidos por um determinado nó da rede alvo, reduzindo assim a transmissão de pacotes capturados redundantes através da rede de monitoramento e, consequentemente, reduzindo consideravelmente o consumo de energia desta rede. Com a agregação de cabeçalhos, apenas as informações presentes nos cabeçalhos dos pacotes capturados são enviadas através da rede de monitoramento. Assim, os cabeçalhos de vários pacotes podem ser enviados na mesma mensagem de monitoramento, reduzindo assim o overhead de transmissão e, consequentemente, reduzindo também o consumo de energia da rede de monitoramento. Experimentos com sensores reais e com um simulador de RSSF são realizados em vários cenários para avaliar o sistema de monitoramento proposto. Os resultados obtidos mostram a eficiência energética do EPMOSt e a viabilidade de utilizá-lo para monitorar RSSF em ambientes reais.

Significant characteristic of any VLSI design circuit is its power, reliability, operating frequency and implementation cost. Dynamic CMOS designs provide high operating speeds compared to static CMOS designs combined with low silicon area requirement. This thesis describes the design and the optimization of high performance carry select adder. Previous researchers believed that existing CSA designs has reached theoretical speed bound. But, only a considerable portion of hardware resources of traditional adders are used in worst case scenario. Based on this observation our proposed design will improve on theoretical limit. The major scope of this proposed design is to increase the speed of carry generation between intermediate blocks of Carry select Adder (CSA) by introducing fast multiple clock Domino Manchester carry chain (MCC) that generates carry outputs. This design technique will have some advantages compared to pre-existing implementations in operating speed and power delay product. Simulation has been done using GPDK (Generic Process Design Kits) technology using cadence virtuoso. Thus the proposed technique provides advantages over pre-existing techniques in terms of operating speed.

Arithmetic units and logic circuits are critical components of any VLSI system. Thus realizing efficient arithmetic units and logic circuits is required for better performance of a data path unit and therefore microprocessor or digital signal processor (DSP). Adders are basic building blocks of any processor or data path application. For the design of high performance processing units, high-speed adders with low power consumption is a requirement. Carry Select Adder (CSA) is known to be one of the fastest adders used in many data processing applications. This first contribution of the dissertation is the design of a new CSA architecture using Manchester carry chain (MCC) in multioutput domino CMOS logic. It employs a novel MCC blocks in a hierarchical approach in the design of the CSA. The proposed MCC block is also extended in designing a power-delay and area efficient Vedic multiplier based on "Urdhva-Tiryakbhyam”. The simulation results shows that the proposed architecture achieves two fold advantages in terms of power-delay product (PDP) and hardware overhead. Apart from adders and multipliers, counters also play a major role in a data path unit. Counters are basic building blocks in many VLSI applications such as timers, memories, ADCs/DACs, frequency dividers etc. It is observed that design of counters has power overhead because of requirement of high power consumption for the clock signal distribution and undesired activity of flip-flops due to presence of clocks. The second contribution of the dissertation is the power efficient design of synchronous counters that reduces the power consumption due to clock distribution for different flip-flops and offers high reliability. The simulation results shows that the proposed counter design has lower power requirement and power-area product than existing counter architectures. Pipelines can be used for achieving high circuit operating speeds. However, as the operating frequency increases, the number of pipeline stages also increase linearly and so the memory elements. The third contribution of the dissertation is the dynamic memory-less pipeline design based on sinusoidal three-phase clocking scheme that reduces the power required by the clock and offers high circuit operating frequencies. Finally, the dissertation presents a novel tool for Boolean-function realization with minimum number of transistor in series. This tool is based on applying a new functional decomposition algorithms to decompose the initial Boolean-function into a network of smaller sub-functions and subsequently generating the final circuit. The effectiveness of proposed technique is estimated using circuit level simulations as well as using automated tool. The number of levels required using proposed technique is reduced by an average of 70% compared to existing techniques.

Papermaking process require large amounts of energy and water; therefore, pulp and paper mills can be potentially very polluting. The wastewater resulting from the papermaking process must be carefully managed as it is very rich in dissolved organic matter and contain compounds that make it difficult to treat. MBBR technology emerged as a possibility to increase the treatment capacity and to make more compact treatment systems designed to remove high organic loads, since in addition to having biomass in suspension as the activated sludge process, also has biomass adhered to plastic supports. This research was commissioned by Stora Enso Kvarnsveden and emerged in need of a bioreactor modification due to reduction of the wastewater load after the closure of PM11 in 2013 and PM8 in 2017. An evaluation of the performance of bioreactors is necessary with possible results to only run one bioreactor in the future and save energy. For performance analysis, two weeks of measurements were performed, one with the two bioreactors running at the same time and one with only K150 reactor running, trying to simulate what happens if one of them is stopped. Analysis of the main operating parameters of the MBBR system were evaluated during this research. As result, it was recommended an action to remove the excess of adhered biomass and an increase of the filling rating to 50%, in order to optimize TOC reduction.

This diploma thesis analyses the market efficiency hypothesis of chosen currency pairs EUR/USD, EUR/CZK and USD/CZK. The aim of this study is to describe the price behaviour of chosen financial assets and verify the random walk hypothesis on the foreign exchange market. Model of random walk says there is no relationship between historical and future prices, so price changes are random and cannot be predicted. Random walk hypothesis was tested by chosen statistic tests runs test, test of auto-correlation, variance ratio test and unit root test (Augmented Dickey-Fuller Test). Data were collected through the online trading platform and tested in EViews. Period of testing for daily changes (D1) was chosen from 31.12.2009 to 29.12.2017 and for weekly changes (T1) from 2.1.2005 to 29.12.2017. This thesis proved weak-form efficiency of EUR/USD and USD/CZK for both daily changes and weekly changes in a chosen period. Inefficient behaviour of daily changes of EUR/CZK (D1) was indicated by runs test, test of autocorrelation and variance ratio test. There is a question what the cause of inefficiency is. The most likely explanation is currency intervention of the Czech National Bank which took place from April 2013 to April 2017 in order to achieve the inflation target and prevent deflation. Traders could also achieve profits by speculating on appreciation of Czech Crown below 27,-crowns/euro which is not in harmony with efficient-market hypothesis. Moreover, currency pair EUR/CZK is not liquid as major currency pairs and there are bigger transaction costs because of bid-offer spread. This work can contribute to next research in connection with results of this study. To verify if the cause of inefficient behaviour of daily price changes of EUR/USD are currency interventions of the Czech National Bank, I would suggest testing efficient-market hypothesis exactly at the time of interventions. It would be also suitable to compare results of different methodologies including testing in short-time intervals of price changes.