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Showing 1 to 20 of 91 for “"Mapreduce"”.
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Text mining with neural network and MapReduce
… The proposed method applies neural network in MapReduce framework which used for parallel programming. This approach has advantage when program works with growing unstructured data on distributed file storage system. The results show that natural language processing method improves …
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Evaluating MapReduce System Performance: A Simulation Approach
… processed is exploding in the Big Data era. The MapReduce system popularized by open-source Hadoop is a powerful tool for the exploding data problem, and is widely employed in many areas involving large scale of data. In many circumstances, hypothetical MapReduce systems must be evaluated, e.g. …
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A MapReduce Framework for Heterogeneous Computing Architectures
… resources by leveraging the power of the MapReduce programming model and OpenCL programming language. We propose a portable architecture-aware framework that efficiently runs an application across heterogeneous resources, specifically AMD GPUs and NVIDIA GPUs, while hiding complex …
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An Efficient Platform for Large-Scale MapReduce Processing
… MMR combines Pthreads, MPI and the Google's MapReduce processing model to support multi-threaded as well as dis- tributed parallelism. Experiments show that our model signi cantly outperforms the leading open-source solution, Hadoop. It demonstrates linear scaling for CPU-intensive processing …
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Design and Implementation of a Serverless MapReduce Framework
… ευέλικτες λύσεις επεξεργασίας. Τα παραδοσιακά MapReduce frameworks εξαρτώνται συνήθως από στατικές υποδομές, οι οποίες μπορεί να είναι δαπανηρές και σύνθετες στην εγκατάσταση. Το serverless computing προσφέρει on-demand εκτέλεση με αυτόματη και παραμετροποιήσιμη κλιμάκωση και παρέχει μια …
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Performance modeling framework for SLO-driven MapReduce environments
Several companies are increasingly using MapReduce for efficient large scale data processing such as personalized advertising, spam detection, and data mining tasks. There is a growing need among MapReduce users to achieve different Service Level Objectives (SLOs). Often, applications need to …
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GreenMap: MapReduce with ultra-high-efficiency power delivery
… stack, we implemented GreenMap, a modified MapReduce framework on top of series stacks, that assigns tasks in synchronization. We evaluated the conversion loss of GreenMap on a small data center. At all loads, GreenMap achieves a 81x-138x reduction in conversion loss from commercial-grade …
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Phurti: application and network-aware flow scheduling for MapReduce
Traffic for a typical MapReduce job in a datacenter consists of multiple network flows. Traditionally, network resources have been allocated to optimize network-level metrics such as flow completion time or throughput. Some recent schemes propose using application-aware scheduling which can reduce …
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Performance guarantees for deadline-driven MapReduce jobs under failure
Restriction data tranferred 2014-07-01T11:36:32-05:00 Original Data Group with Access Administrator Release Date: 2015-08-22 11:57:26 UTC Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system
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Sharing the love : a generic socket API for Hadoop Mapreduce
… computations on a cluster. It includes Hadoop MapReduce and the Hadoop Distributed File System (HDFS). HDFS has known scalability limitations due to its single NameNode which holds the entire file system namespace in RAM on one computer. Therefore, the NameNode can only store limited amounts of …
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Data analytics with mapreduce in apache spark and hadoop systems
MapReduce comes from a traditional problem solving method: separating a big problem and solving each small parts. With the target of computing larger dataset in more efficient and cheaper way, this is implement into a programming mode to deal with massive quantity of data. The users get a map …
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Scaling simple, compact and extended compact genetic algorithms using MapReduce
… execution profiles, can be translated into the MapReduce paradigm. Results show that (1) Hadoop is an excellent choice to push evolutionary computation boundaries on very large problems, and (2) that transparent linear speedups are possible without changing the underlying data-intensive flow …
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Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture
… To address this problem, several parallel MapReduce based implementations of SVMs have been proposed. These implementation have in common that they decompose a large-scale multi-class problem to a number of relatively smaller subproblems by dividing the data into multiple partitions which …
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Evaluating a Cluster of Low-Power ARM64 Single-Board Computers with MapReduce
… datasets have become more crucial than ever. MapReduce is a restricted programing model for expressing parallel computations as simple serial functions, and an execution framework for distributing those computations over large datasets residing on clusters of commodity hardware. MapReduce …
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Adaptation of the MapReduce programming framework to compute-intensive data-analytics kernels
… a vision of CIDA applications programmed in a MapReduce style framework and running on clusters of accelerators. Regardless of the type of accelerator, whether GPUs (NVIDIA or AMD), other manycore architectures (like Intel Larrabee or MIC) or even heterogeneous chips (AMD Fusion or IBM Cell …
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Mapreduce and heterogeneity: power-aware bag-of-tasks, framework parameter sensitivity, and dynamic cluster aware framework configuration
… presents the techniques for adaptation of MapReduce frameworks to incorporate heterogeneity-aware scheduling algorithms, an inspection of cluster configurations and how they impact these scheduling algorithms, an analysis regarding how the cluster configuration and the heterogeneity-aware …
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On the Feasibility of MapReduce to Compute Phase Space Properties of Graphical Dynamical Systems: An Empirical Study
… challenges. To address this, we devise various MapReduce programming paradigms that can be used to characterize system state transitions, compute phase spaces, functional equivalence classes, dynamic equivalence classes and cycle equivalence classes of dynamical systems. We also evaluate these …
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Functional testing techniques for new massive data processing paradigms
… de los datos procesados. Entre estos, se destaca MapReduce que permite procesar grandes cantidades de datos en una infraestructura distribuida que puede cambiar durante la ejecución debido a los frecuentes fallos en la infraestructura y las optimizaciones. El desarrollador sólo diseña el programa, …
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I2MAPREDUCE: DATA MINING FOR BIG DATA
<p>This project is an extension of i<sup>2</sup>MapReduce: Incremental MapReduce for Mining Evolving Big Data . i<sup>2</sup>MapReduce is used for incremental big data processing, which uses a fine-grained incremental engine, a general purpose iterative model that includes iteration algorithms such …
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Application-Aware Network Design Using Software Defined Networking for Application Performance Optimization for Big Data and Video Streaming
… applications, we consider two classes: Hadoop MapReduce and video streaming. The Hadoop MapReduce (M/R) framework has become the de facto standard for Big Data analytics. However, the lack of network-awareness of the default MapReduce resource manager in a traditional IP network can cause …
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