2017 2nd International Conference on Communication and Electronics Systems (ICCES) 2017
DOI: 10.1109/cesys.2017.8321202
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A comparison study and performance evaluation of schedulers in Hadoop YARN

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Cited by 5 publications
(5 citation statements)
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“…In the case of Hadoop, previous research has been conducted to predict the execution time of a job in Hadoop. Subbulakshmi and Manjaly [22] and Hou et al [23] show the performance improvement of Hadoop based on job scheduling using Hadoop YARN. However, this study did not attempt to optimize the performance using the developed mathematical model.…”
Section: A Apache Hadoop System Modelmentioning
confidence: 99%
“…In the case of Hadoop, previous research has been conducted to predict the execution time of a job in Hadoop. Subbulakshmi and Manjaly [22] and Hou et al [23] show the performance improvement of Hadoop based on job scheduling using Hadoop YARN. However, this study did not attempt to optimize the performance using the developed mathematical model.…”
Section: A Apache Hadoop System Modelmentioning
confidence: 99%
“…The Fair scheduler assigns resources to applications such that each application gets, on average, an equal dominant share of resources over time [45]. There are Customized scheduling policies over default schedulers for specific applications to offer performance improvements compared to the default policies [37]. One of these extended policies is Dominant Resource Fairness (DRF).…”
Section: Introductionmentioning
confidence: 99%
“…Oldest jobs are chosen first from the queue. FIFO is simple and works well with small jobs but is not [40,37].…”
Section: Introductionmentioning
confidence: 99%
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