2016 International Conference on Computing, Analytics and Security Trends (CAST) 2016
DOI: 10.1109/cast.2016.7914976
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Towards optimization of Hadoop Map reduce jobs on cloud

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Cited by 3 publications
(4 citation statements)
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“…Dealing with a large volume of data, several studies revealed the use of Hadoop for processing big data. It is vital that such large number of datasets should be adjusted to handle the dynamic process of workload through the existing computer resources [12,14,15]. A Hadoop cluster with a fixed size is shown to have problems when processing the streaming data and it is also not cost-effective for the cloud resources since it does not use fully its resources when the data load on the cluster is low.…”
Section: Related Workmentioning
confidence: 99%
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“…Dealing with a large volume of data, several studies revealed the use of Hadoop for processing big data. It is vital that such large number of datasets should be adjusted to handle the dynamic process of workload through the existing computer resources [12,14,15]. A Hadoop cluster with a fixed size is shown to have problems when processing the streaming data and it is also not cost-effective for the cloud resources since it does not use fully its resources when the data load on the cluster is low.…”
Section: Related Workmentioning
confidence: 99%
“…However, this approach is not as flexible as AWS and is also not cost-effective [9]. Additionally, in the traditional Hadoop cluster with a First-In First-Out (FIFO) scheduler, all task slots for a job in the cluster blocks other jobs to use the resources until the current scheduled job finishes, which may cause a bottleneck in processing the data [14], especially when dealing with massive data (e.g., Twitter) arriving at the same time. As one of the biggest social platforms, Twitter provides a huge data set which can be streamed for sentiment analysis.…”
Section: Related Workmentioning
confidence: 99%
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