Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-06-01T19:08:36Z No. of bitstreams: 1 VictorNogueiraTeixeiraMota.pdf: 10378249 bytes, checksum: 880b9599e08ab5e5f7a0bd7c97ddcb71 (MD5) / Made available in DSpace on 2017-06-01T19:08:36Z (GMT). No. of bitstreams: 1 VictorNogueiraTeixeiraMota.pdf: 10378249 bytes, checksum: 880b9599e08ab5e5f7a0bd7c97ddcb71 (MD5) Previous issue date: 2016-03-31 / Housing with dignity in Brazil still does not reach the entire population. There are people who live in properties in precarious situations of safety and health. Besides not having a comfortable shelter to live, many people have no access to electricity and precious to the sustainable development of the population. This work is discussed and developed a rural housing model that meets low cost criteria, environmental comfort for users and energy efficiency. The National Program for Rural Housing the federal government aims to eradicate substandard housing, but housing conditions the granting of the prior existence of electricity in the community, which in many cases is isolated and does not have a utility power network. This work is raising questions about the rural electrification through a comparative analysis of models and incentive policies for PV solar energy in Brazil and worldwide, identifying strengths and to improve the energy matrix of the country, serving isolated households in urban centers and ensuring quality of life for these citizens. The main motivation is to give an indication for rural housing policies, in order to facilitate the extension of this benefit to under served rural sectors by electrification programs, whose condition is mandatory for eligibility of rural housing programs. The results encourage the spread of solar PV technology as an alternative and promising renewable source in Brazil. / Habitação com dignidade no Brasil ainda não atinge a totalidade da população. Há pessoas que habitam imóveis em situações precárias de segurança e salubridade. Além de não terem um abrigo confortável para residir, muitas pessoas não tem acesso a energia eletrica, bem precioso para o desenvolvimento sustentável da população. Neste trabalho é discutido e desenvolvido um modelo de habitação rural que atende critérios de baixo custo, de conforto ambiental para os usuários e de eficiência energética. O Programa Nacional de Habitação Rural do governo federal visa a erradicação de habitações precárias, porém, condiciona a concessão da moradia a existência prévia de energia elétrica na comunidade, a qual em muitos casos e isolada e não possui rede de energia da concessionária. Este trabalho vem levantar questões acerca da eletrificação rural, através de uma análise comparativa dos modelos e políticas de incentivo a energia solar fotovoltaica no Brasil e no mundo, identificando pontos positivos e a melhorar a matriz elétrica do país, atendendo domicílios isolados dos centros urbanos e garantindo qualidade de vida a estes cidadãos. A principal motivação é dar um indicativo para as políticas de habitação rural, de forma a viabilizar a extensão deste beneficio para setores rurais desassistidos por programas de eletrificação, cuja condição é mandatória para elegibilidade de programas de habitação rural. Os resultados obtidos fomentam a disseminação da tecnologia de energia solar fotovoltaica como fonte renovável alternativa e bastante promissora no Brasil.

High performance grid computing is a key enabler of large scale collaborative computational science. With the promise of exascale computing, high performance grid systems are expected to incur electricity bills that grow super-linearly over time. In order to achieve cost effectiveness in these systems, it is essential for the scheduling algorithms to exploit electricity price variations, both in space and time, that are prevalent in the dynamic electricity price markets. Typically, a job submission in the batch queues used in these systems incurs a variable queue waiting time before the resources necessary for its execution become available. In variably-priced electricity markets, the electricity prices fluctuate over discrete intervals of time. Hence, the electricity prices incurred during a job execution will depend on the start and end time of the job. Our thesis consists of two parts. In the first part, we develop a method to predict the start and end time of a job at each system in the grid. In batch queue systems, similar jobs which arrive during similar system queue and processor states, experience similar queue waiting times. We have developed an adaptive algorithm for the prediction of queue waiting times on a parallel system based on spatial clustering of the history of job submissions at the system. We represent each job as a point in a feature space using the job characteristics, queue state and the state of the compute nodes at the time of job submission. For each incoming job, we use an adaptive distance function, which assigns a real valued distance to each history job submission based on its similarity to the incoming job. Using a spatial clustering algorithm and a simple empirical characterization of the system states, we identify an appropriate prediction model for the job from among standard deviation minimization method, ridge regression and k-weighted average. We have evaluated our adaptive prediction framework using historical production workload traces of many supercomputer systems with varying system and job characteristics, including two Top500 systems. Across workloads, our predictions result in up to 22% reduction in the average absolute error and up to 56% reduction in the percentage prediction errors over existing techniques. To predict the execution time of a job, we use a simple model based on the estimate of job runtime provided by the user at the time of job submission. In the second part of the thesis, we have developed a metascheduling algorithm that schedules jobs to the individual batch systems of a grid, to reduce both the electricity prices for the systems and response times for the users. We formulate the metascheduling problem as a Minimum Cost Maximum Flow problem and leverage execution period and electricity price predictions to accurately estimate the cost of job execution at a system. The network simplex algorithm is used to minimize the response time and electricity cost of job execution using an appropriate flow network. Using trace based simulation with real and synthetic workload traces, and real electricity price data sets, we demonstrate our approach on two currently operational grids, XSEDE and NorduGrid. Our experimental setup collectively constitute more than 433K processors spread across 58 compute systems in 17 geographically distributed locations. Experiments show that our approach simultaneously optimizes the total electricity cost and the average response time of the grid, without being unfair to users of the local batch systems. Considering that currently operational HPC systems budget millions of dollars for annual operational costs, our approach which can save $167K in annual electricity bills, compared to a baseline strategy, for one of the grids in our test suite with over 76000 cores, is very relevant for reducing grid operational costs in the coming years.