Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort Deneyleri

Hadoop framework employs MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over...

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Main Authors: G. Ozen, R. Sultanov
Format: Article
Language:English
Published: Kyrgyz Turkish Manas University 2015-05-01
Series:MANAS: Journal of Engineering
Subjects:
Online Access:https://dergipark.org.tr/en/download/article-file/575941
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author G. Ozen
R. Sultanov
author_facet G. Ozen
R. Sultanov
author_sort G. Ozen
collection DOAJ
description Hadoop framework employs MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over nodes. The number of nodes in the cluster affect the execution time of jobs. Main idea of this paper is to determine how number of nodes affect the performance of Hadoop framework on a cloud environment with using benchmarking tools. For this purpose, various tests are carried out on a Hadoop cluster with 10 nodes hosted on a cloud environment by running Terabyte Sort benchmarking tools on it. According to test results, increasing number of nodes improves job execution performance of Hadoop framework and reduces job execution time.
format Article
id doaj-art-a83531aecde44d3f85b4a0a6613acd14
institution Kabale University
issn 1694-7398
language English
publishDate 2015-05-01
publisher Kyrgyz Turkish Manas University
record_format Article
series MANAS: Journal of Engineering
spelling doaj-art-a83531aecde44d3f85b4a0a6613acd142024-12-05T04:28:34ZengKyrgyz Turkish Manas UniversityMANAS: Journal of Engineering1694-73982015-05-013111201437Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort DeneyleriG. OzenR. SultanovHadoop framework employs MapReduce programming paradigm to process big data by distributing data across a cluster and aggregating. MapReduce is one of the methods used to process big data hosted on large clusters. In this method, jobs are processed by dividing into small pieces and distributing over nodes. The number of nodes in the cluster affect the execution time of jobs. Main idea of this paper is to determine how number of nodes affect the performance of Hadoop framework on a cloud environment with using benchmarking tools. For this purpose, various tests are carried out on a Hadoop cluster with 10 nodes hosted on a cloud environment by running Terabyte Sort benchmarking tools on it. According to test results, increasing number of nodes improves job execution performance of Hadoop framework and reduces job execution time.https://dergipark.org.tr/en/download/article-file/575941big datamapreducehadoop.büyük verimapreducehadoop
spellingShingle G. Ozen
R. Sultanov
Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort Deneyleri
MANAS: Journal of Engineering
big data
mapreduce
hadoop.
büyük veri
mapreduce
hadoop
title Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort Deneyleri
title_full Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort Deneyleri
title_fullStr Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort Deneyleri
title_full_unstemmed Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort Deneyleri
title_short Hadoop Çatısının Bulut Ortamında Gerçeklenmesi Ve Terabyte Sort Deneyleri
title_sort hadoop catisinin bulut ortaminda gerceklenmesi ve terabyte sort deneyleri
topic big data
mapreduce
hadoop.
büyük veri
mapreduce
hadoop
url https://dergipark.org.tr/en/download/article-file/575941
work_keys_str_mv AT gozen hadoopcatısınınbulutortamındagerceklenmesiveterabytesortdeneyleri
AT rsultanov hadoopcatısınınbulutortamındagerceklenmesiveterabytesortdeneyleri