Página 1 dos resultados de 351 itens digitais encontrados em 0.002 segundos

## Simulação e estudo da plataforma Hadoop MapReduce em ambientes heterogêneos; Simulation and study of the hadoop mapreduce platform on heterogeneous environments

Kolberg, Wagner
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Trabalho de Conclusão de Curso Formato: application/pdf
POR
Relevância na Pesquisa
37.96%

## Maresia : an approach to deal with the single points of failure of the MapReduce model; Maresi: uma abordagem para lidar com os pontos de falha única do modelo MapReduce

Marcos, Pedro de Botelho
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
ENG
Relevância na Pesquisa
37.74%

## Adequação da computação intensiva em dados para ambientes desktop grid com uso de MapReduce; Adequacy of intensive data computing to desktop grid environment with using of mapreduce

Anjos, Julio Cesar Santos dos
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
37.95%

## Aplicação do MapReduce na análise de mutações gênicas de pacientes; Application of mapreduce in the analysis of genetic mutations in patients

Reckziegel Filho, Bruno
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Trabalho de Conclusão de Curso Formato: application/pdf
POR
Relevância na Pesquisa
37.54%

## Caracterização do consumo energético do Hadoop MapReduce; Characterization of Hadoop’s MapReduce energetic consumption

Rodrigues, Flavio Alles
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Trabalho de Conclusão de Curso Formato: application/pdf
POR
Relevância na Pesquisa
37.69%

## Loop parallelization in the cloud using OpenMP and MapReduce; Paralelização de laços na nuvem usando OpenMP e MapReduce

Rodolfo Guilherme Wottrich
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 09/04/2014 PT
Relevância na Pesquisa
37.63%
A busca por paralelismo sempre foi um importante objetivo no projeto de sistemas computacionais, conduzida principalmente pelo constante interesse na redução de tempos de execução de aplicações. Programação paralela é uma área de pesquisa ativa, na qual o interesse tem crescido devido à emergência de arquiteturas multicore. Por outro lado, aproveitar as grandes capacidades de computação e armazenamento da nuvem e suas características desejáveis de flexibilidade e escalabilidade oferece várias oportunidades interessantes para abordar problemas de pesquisa relevantes em computação científica. Infelizmente, em muitos casos a implementação de aplicações na nuvem demanda conhecimento específico de interfaces de programação paralela e APIs, o que pode se tornar um fardo na programação de aplicações complexas. Para superar tais limitações, neste trabalho propomos OpenMR, um modelo de execução baseado na sintaxe e nos princípios da API OpenMP que facilita a tarefa de programar sistemas distribuídos (isto é, clusters locais ou a nuvem remota). Especificamente, este trabalho aborda o problema de executar a paralelização de laços, usando OpenMR, em um ambiente distribuído, através do mapeamento de iterações do laço para nós MapReduce. Assim...

## Inclusão de funcionalidades MapReduce em sistemas de data warehousing

Silva, Dário Almeno Matos da
Tipo: Dissertação de Mestrado
Publicado em 18/12/2013 POR
Relevância na Pesquisa
37.87%

## Hadoop MapReduce tolerante a faltas bizantinas

Costa, Pedro Alexandre Reis Sá da Costa
Tipo: Dissertação de Mestrado
Publicado em //2011 POR
Relevância na Pesquisa
37.78%

## Distributed processing of large remote sensing images using MapReduce - A case of Edge Detection

Tesfamariam, Ermias Beyene
Fonte: Universidade Nova de Lisboa Publicador: Universidade Nova de Lisboa
Tipo: Dissertação de Mestrado
Publicado em 07/02/2011 ENG
Relevância na Pesquisa
37.84%
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.; Advances in sensor technology and their ever increasing repositories of the collected data are revolutionizing the mechanisms remotely sensed data are collected, stored and processed. This exponential growth of data archives and the increasing user’s demand for real-and near-real time remote sensing data products has pressurized remote sensing service providers to deliver the required services. The remote sensing community has recognized the challenge in processing large and complex satellite datasets to derive customized products. To address this high demand in computational resources, several efforts have been made in the past few years towards incorporation of high-performance computing models in remote sensing data collection, management and analysis. This study adds an impetus to these efforts by introducing the recent advancements in distributed computing technologies, MapReduce programming paradigm, to the area of remote sensing. The MapReduce model which is developed by Google Inc. encapsulates the efforts of distributed computing in a highly simplified single library. This simple but powerful programming model can provide us distributed environment without having deep knowledge of parallel programming. This thesis presents a MapReduce based processing of large satellite images a use case scenario of edge detection methods. Deriving from the conceptual massive remote sensing image processing applications...

## MRSG – a MapReduce simulator over SimGrid

Kolberg, Wagner; Marcos, Pedro de Botelho; Anjos, Julio Cesar Santos dos; Miyazaki, Alexandre Kenta Salgueiro; Geyer, Claudio Fernando Resin; Arantes, Luciana Bezerra
Fonte: Universidade Federal do Rio Grande Publicador: Universidade Federal do Rio Grande
Tipo: Artigo de Revista Científica
ENG
Relevância na Pesquisa
37.63%
MapReduce is a parallel programming model to process large datasets, and it was inspired by the Map and Reduce primitives from functional languages. Its first implementation was designed to run on large clusters of homogeneous machines. Though, in the last years, the model was ported to different types of environments, such as desktop grid and volunteer computing. To obtain a good performance in these environments, however, it is necessary to adapt some framework mechanisms, such as scheduling and data distribution algorithms. In this paper we present the MRSG simulator, which reproduces the MapReduce work-flow on top of the SimGrid simulation toolkit, and provides an API to implement and evaluate these new algorithms and policies for MapReduce. To evaluate the simulator, we compared its behavior against a real Hadoop MapReduce deployment. The results show an important similarity between the simulated and real executions.

## Uma abordagem para o teste de dependabilidade de sistemas MapReduce com base em casos de falha representativos

Marynowski, Joao Eugenio
Fonte: Universidade Federal do Paraná Publicador: Universidade Federal do Paraná
Tipo: Tese de Doutorado Formato: application/pdf
PORTUGUêS
Relevância na Pesquisa
27.74%

## Execução paralela de programação genética utilizando MapReduce

Fonte: Universidade Federal de Lavras Publicador: Universidade Federal de Lavras
Tipo: Trabalho de Conclusão de Curso
PT_BR
Relevância na Pesquisa
37.42%
The Genetic Programming is a technique used for automatic generation of applications in Wireless Sensor Networks, which needs to perform a number of simulations for a given problem in order to have a greater degree of confidence of the result obtained by the method. Thus, its running time becomes high when using a single machine. However, there are opportunities for parallelization of these executions that might imply a reduction in execution time and improving the quality of the results. This work is a study on the MapReduce programming model adapted for a Genetic Programming to automatic generation of applications in Wireless Sensor Network (WSN), through the distribution of executions among the machines of a cluster. It proposed an implementation of a Genetic Programming to automatic generation of applications in WSN and used WSN simulator to evaluate the quality of the solution. This study also analyzes the benefits of using the MapReduce framework.

## Avaliação do framework mapreduce para paralelização do algoritmo apriori

Fonte: Universidade Federal de Lavras Publicador: Universidade Federal de Lavras
Tipo: Trabalho de Conclusão de Curso
PT_BR
Relevância na Pesquisa
37.25%
The frequent-patterns mining is an area of extensive use in computing, its your objective is to find information about relevant patterns in large amounts of data. But the main algorithms for frequent-patterns mining have a high execution time, due to the large volume of data they work with. Therefore, parallel programming and frameworks that use this concept seem a good solution to reduce the execution time and level of computing required by these algorithms. This work proposes the parallel and distributed implementation of the Apriori algorithm, well known in the research area of frequent-patterns mining, using MapReduce Framework. The results were compared with the DMTA algorithm (Distributed Multithread Apriori), which also implements the Apriori algorithm in distributed and parallel, but using MPI and OpenMP libraries to create and manage processes and threads

## OPTAS: optimal data placement in MapReduce

Wang, C.; Qin, Y.; Huang, Z.; Peng, Y.; Li, D.; Li, H.
Fonte: IEEE; Online Publicador: IEEE; Online
Tipo: Conference paper
Publicado em //2013 EN
Relevância na Pesquisa
37.42%
The data placement strategy greatly affects the efficiency of MapReduce. The current strategy only takes the map phase into account to optimize the map time. But the ignored shuffle phase may increase the total running time significantly in many jobs. We propose a new data placement strategy, named OPTAS, which optimizes both the map and shuffle phases to reduce their total time. However, the huge search space makes it difficult to find out an optimal data placement instance (DPI) rapidly. To address this problem, an algorithm is proposed which can prune most of the search space and find out an optimal result quickly. The search space firstly is segmented in ascending order according to the potential map time. Within each segment, we propose an efficient method to construct a local optimal DPI with the minimal total time of both the map and shuffle phases. To find the global optimal DPI, we scan the local optimal DPIs in order. We have proven that the global optimal DPI can be found as the first local optimal DPI whose total time stops decreasing, thus further pruning the search space. In practice, we find that at most fourteen local optimal DPIs are scanned in tens of thousands of segments with the pruning strategy. Extensive experiments with real trace data verify not only the theoretic analysis of our pruning strategy and construction method but also the optimality of OPTAS. The best improvements obtained in our experiments can be over 40% compared with the existing strategy used by MapReduce.; Changjian Wang...

## Estudio sobre algoritmos gen??ticos en la nube y el modelo de programaci??n MapReduce

Mu??oz, G.; Garc??a-S??nchez, Pablo; Castillo Valdivieso, Pedro; Garc??a Arenas, Mar??a Isabel; Mora Garc??a, Antonio Miguel; Merelo Guerv??s, Juan Juli??n
Tipo: Artigo de Revista Científica
SPA
Relevância na Pesquisa
37.63%
Este trabajo presenta el proyecto fin de carrera ???Estudio sobre algoritmos gen??ticos en la nube y el modelo de programaci??n MapReduce???. Durante el desarrollo de este proyecto se investig?? en el uso y aplicaci??n de Algoritmos Gen??ticos en distintos entornos de Cloud Computing, como el MapReduce o virtualizaci??n de instancias. Se ejecutaron distintas configuraciones de par??metros del algoritmo (como el tama??o de poblaci??n o el tipo de crossover) en distintas instancias de Amazon Web Services. Los resultados muestran el efecto de estos par??metros al tipo de instancia utilizada.; This paper shows the final degree project ???A study of genetic algorithms in the cloud and the MapReduce model???. During the development of this project the usage and application of genetic algorithms in different Cloud Computing environments was investigated, such as MapReduce or virtualization. Different parameter configurations, such as the population size or crossover type, were launched in different instances of Amazon Web Services. Results show the effect of these parameters to the different types of used instances.

## Constructing Secure MapReduce Framework in Cloud-based Environment

Wang, Yongzhi
Fonte: FIU Digital Commons Publicador: FIU Digital Commons
Tipo: Artigo de Revista Científica Formato: application/pdf
Relevância na Pesquisa
37.82%
MapReduce, a parallel computing paradigm, has been gaining popularity in recent years as cloud vendors offer MapReduce computation services on their public clouds. However, companies are still reluctant to move their computations to the public cloud due to the following reason: In the current business model, the entire MapReduce cluster is deployed on the public cloud. If the public cloud is not properly protected, the integrity and the confidentiality of MapReduce applications can be compromised by attacks inside or outside of the public cloud. From the result integrity’s perspective, if any computation nodes on the public cloud are compromised,thosenodes can return incorrect task results and therefore render the final job result inaccurate. From the algorithmic confidentiality’s perspective, when more and more companies devise innovative algorithms and deploy them to the public cloud, malicious attackers can reverse engineer those programs to detect the algorithmic details and, therefore, compromise the intellectual property of those companies. In this dissertation, we propose to use the hybrid cloud architecture to defeat the above two threats. Based on the hybrid cloud architecture, we propose separate solutions to address the result integrity and the algorithmic confidentiality problems. To address the result integrity problem...

## Sorting, Searching, and Simulation in the MapReduce Framework

Goodrich, Michael T.; Sitchinava, Nodari; Zhang, Qin
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
27.78%
In this paper, we study the MapReduce framework from an algorithmic standpoint and demonstrate the usefulness of our approach by designing and analyzing efficient MapReduce algorithms for fundamental sorting, searching, and simulation problems. This study is motivated by a goal of ultimately putting the MapReduce framework on an equal theoretical footing with the well-known PRAM and BSP parallel models, which would benefit both the theory and practice of MapReduce algorithms. We describe efficient MapReduce algorithms for sorting, multi-searching, and simulations of parallel algorithms specified in the BSP and CRCW PRAM models. We also provide some applications of these results to problems in parallel computational geometry for the MapReduce framework, which result in efficient MapReduce algorithms for sorting, 2- and 3-dimensional convex hulls, and fixed-dimensional linear programming. For the case when mappers and reducers have a memory/message-I/O size of $M=\Theta(N^\epsilon)$, for a small constant $\epsilon>0$, all of our MapReduce algorithms for these applications run in a constant number of rounds.; Comment: 16 pages

## ReStore: Reusing Results of MapReduce Jobs

Elghandour, Iman; Aboulnaga, Ashraf
Tipo: Artigo de Revista Científica
Relevância na Pesquisa
27.78%
Analyzing large scale data has emerged as an important activity for many organizations in the past few years. This large scale data analysis is facilitated by the MapReduce programming and execution model and its implementations, most notably Hadoop. Users of MapReduce often have analysis tasks that are too complex to express as individual MapReduce jobs. Instead, they use high-level query languages such as Pig, Hive, or Jaql to express their complex tasks. The compilers of these languages translate queries into workflows of MapReduce jobs. Each job in these workflows reads its input from the distributed file system used by the MapReduce system and produces output that is stored in this distributed file system and read as input by the next job in the workflow. The current practice is to delete these intermediate results from the distributed file system at the end of executing the workflow. One way to improve the performance of workflows of MapReduce jobs is to keep these intermediate results and reuse them for future workflows submitted to the system. In this paper, we present ReStore, a system that manages the storage and reuse of such intermediate results. ReStore can reuse the output of whole MapReduce jobs that are part of a workflow...

## Improving memory hierarchy performance on mapreduce frameworks for multi-core architectures

de Souza Ferreira, Tharso
Fonte: [Barcelona] : Universitat Autònoma de Barcelona, Publicador: [Barcelona] : Universitat Autònoma de Barcelona,
Tipo: Tesis i dissertacions electròniques; info:eu-repo/semantics/doctoralThesis Formato: application/pdf
Publicado em //2014 ENG; ENG
Relevância na Pesquisa
27.84%