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عنوان فارسی مقاله:
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عنوان انگلیسی مقاله:
A Dynamic Data Placement Strategy for Hadoop in Heterogeneous Environments
سال انتشار : 2014
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مقدمه انگلیسی مقاله:
1. Introduction
In recent years, with the rapid development of the Internet, network service has become one of the most frequently used computer applications. Search engine, webmail, and social network services are currently indispensable data-intensive applications. Because increasingly more people use web services, processing a large amount of data efficiently can be a substantial problem. Currently, the method for processing a large amount of data involves adopting parallel computing. In 2004, Google proposed MapReduce [10]. Since then the Google File System [11] and Bigtable [8] have used MapReduce to construct a data center that can process at least 20 petabytes a day. Because of the scalability, simplicity, and fault tolerance of the MapReduce model, it is frequently used in parallel data processing in large-scale clusters. Yahoo! [6] is the main developer of Hadoop, which is the most famous open source that implements the Google MapReduce model [2,3]. Hadoop isused to process hundreds of terabytes of data on Linux with 10,000 cores. In addition, Facebook [7] and Amazon [1] have also adopted Hadoop to manage and process large amounts of data. MapReduce exhibits several advantages that differ from those of traditional parallel computing systems. First, regarding scalability, even when new machines are added to a cluster, the system still works well without reconstruction or much modification. Second, regarding fault tolerance, the MapReduce model can automatically manage failures and mitigate complexity of fault tolerance mechanisms. When a machine fails, MapReduce moves the task that was run on the failed machine to be rerun on another machine. Third, regarding simplicity, programmers can use the MapReduce model without needing to understand thoroughly the details of parallel distributed programming. A program executed using the MapReduce model partitions jobs into a numerous tasks to be assigned and run on multiple nodes in the cluster, and the program collects the processing results of each node to be return.
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کلمات کلیدی:
SLDP: A Novel Data Placement Strategy for Large-Scale ... - IEEE Xplore ieeexplore.ieee.org/document/7176066/ by R Xiong - 2014 - Cited by 3 - Related articles Hadoop as a popular open-source implementation of MapReduce is widely used ... in heterogeneous environments by using default data placement strategy of ... [PDF]An Efficient Data Placement AlgorithmFor Hadoop Heterogeneous ... ijiset.com/vol2/v2s5/IJISET_V2_I5_63.pdf heterogeneous Hadoop cluster. ... heterogeneous environment, some tasks run faster on .... The hadoop default data placement strategy is supposed to ... Adaptive Dynamic Data Placement Algorithm for Hadoop in ... https://www.researchgate.net/.../313386392_Adaptive_Dynamic_Data_Placement_Algor... Feb 7, 2017 - placement strategy of Hadoop can boost the. e ciency of the MapReduce model, but in a. heterogeneous environment, such data placement. [PDF]Analysis of Dynamic Data Placement Strategy for Heterogeneous ... www.ijettcs.org/Volume4Issue4/IJETTCS-2015-08-10-64.pdf by R Jain - Related articles on a Hadoop MapReduce cluster, our Data placement theme adaptively balances .... performance in heterogeneous computing environments is to considerably ... PDF (517 K) - Journal of Advances in Computer Engineering and ... jacet.srbiau.ac.ir/article_9692_6677b154fb32e22c403dbc9fdba016ec.pdf by A Sharafi - 2016 - Related articles Dec 11, 2016 - strategy of Hadoop can boost the efficiency of the MapReduce model, but in a heterogeneous environment, such data placement has many. Article Info - Journal of Advances in Computer Engineering and ... jacet.srbiau.ac.ir/article_9692_0.html by A Sharafi - 2016 - Related articles Default Hadoop doesn't consider load state of each node in distribution input data ... and may increase extra energy dissipation in heterogeneous environments. ... data placement strategy in MapReduce in the homogeneous Hadoop cluster ...
