881 resultados para grid databases


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The technologies are advancing at a pace so expressive that allow the increase of the power quality from generation until the distribution to end customers. This improvement has been made possible through the automation of the energy that follows to a better quality of the energy provided, a lower energy supply disruptions and a very short recovery time. The trend of today and the near future is the distributed energy generation. To keep the automated control of the chain, the presence of Smart Grids is needed and that will be the most efficient and economical way to manage the entire system. Within this theme, is going to be necessary analyze the electric cars that promise to promote a more sustainable transport because it doesn’t uses fossil fuels, and more healthy because it does not emit pollutants into the atmosphere. The popularization of this type of vehicle is estimated to happen in a few decades and the case study analyzing its influence on the demand of the electrical system is something that will be very important in the near future. This paper presents a study of the influence of the inclusion of charges refering to electric cars

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The use of computational grid simulators is particularly important for studying the algorithms of task scheduling. Through the simulators it’s possible to assess and compare the performance of different algorithms in various scenarios. Despite the simulation tools provide basic features for simulation in distributed environments, they don’t offer internal policies of task scheduling, so that the implementation of the algorithms must be realized by the user himself. Therefore, this study aims to present the library of task scheduling LIBTS (LIBrary Tasks Scheduling) which is developed and adapted to the SimGrid simulator to provide the users with a tool to analyze the algorithms of task scheduling in the computational grid.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Pós-graduação em Engenharia Elétrica - FEIS

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

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Data-intensive Grid applications require huge data transfers between grid computing nodes. These computing nodes, where computing jobs are executed, are usually geographically separated. A grid network that employs optical wavelength division multiplexing (WDM) technology and optical switches to interconnect computing resources with dynamically provisioned multi-gigabit rate bandwidth lightpath is called a Lambda Grid network. A computing task may be executed on any one of several computing nodes which possesses the necessary resources. In order to reflect the reality in job scheduling, allocation of network resources for data transfer should be taken into consideration. However, few scheduling methods consider the communication contention on Lambda Grids. In this paper, we investigate the joint scheduling problem while considering both optical network and computing resources in a Lambda Grid network. The objective of our work is to maximize the total number of jobs that can be scheduled in a Lambda Grid network. An adaptive routing algorithm is proposed and implemented for accomplishing the communication tasks for every job submitted in the network. Four heuristics (FIFO, ESTF, LJF, RS) are implemented for job scheduling of the computational tasks. Simulation results prove the feasibility and efficiency of the proposed solution.

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In the developed world, grid-connected photovoltaics (PVs) are the fastest-growing segment of the energy market. From 1999 to 2009, this industry had a 42% compound annual growth-rate. From 2009 to 2013, it is expected to grow to 45%, and in 2013 the achievement of grid parity - when the cost of solar electricity becomes competitive with conventional retail (including taxes and charges) grid-supplied electricity - is expected in many places worldwide. Grid-connected PV is usually perceived as an energy technology for developed countries, whereas isolated, stand-alone PV is considered as more suited for applications in developing nations, where so many individuals still lack access to electricity. This rationale is based on the still high costs of PV when compared with conventional electricity. We make the case for grid-connected PV generation in Brazil, showing that with the declining costs of PV and the rising prices of conventional electricity, urban populations in Brazil will also enjoy grid parity in the present decade. We argue that governments in developing nations should act promptly and establish the mandates and necessary conditions for their energy industry to accumulate experience in grid-connected PV, and make the most of this benign technology in the near future. (C) 2010 Elsevier Ltd. All rights reserved.

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This paper proposes three new hybrid mechanisms for the scheduling of grid tasks, which integrate reactive and proactive approaches. They differ by the scheduler used to define the initial schedule of an application and by the scheduler used to reschedule the application. The mechanisms are compared to reactive and proactive mechanisms. Results show that hybrid approach produces performance close to that of the reactive mechanisms, but demanding less migrations.

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Fractures of the mandibular angle deserve particular attention because they represent the highest percentage of mandibular fractures and have the highest postsurgical complication rate, making them the most challenging and unpredictable mandibular fractures to treat. Despite the evolution in the treatment of maxillofacial trauma and fixation methods, no single treatment modality has been revealed to be ideal for mandibular angle fractures. Several methods of internal fixation have been studied with great variation in complications rates, especially postoperative infections. Recently, new studies have shown reduction of postsurgical complications rates using three-dimensional plates to treat mandibular angle fractures. Nevertheless, only few surgeons have used this type of plate for the treatment of mandibular angle fractures. The aim of this clinical report was to describe a case of a patient with a mandibular angle fracture treated by an intraoral approach and a three-dimensional rectangular grid miniplate with 4 holes, which was stabilized with monocortical screws. The authors show a follow-up of 8 months, without infection and with occlusal stability.

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Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.