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Using Hadoop MapReduce in a multicluster environment
Jožef Stefan Institute, Ljubljana, Slovenia.
Jožef Stefan Institute, Ljubljana, Slovenia.
Jožef Stefan Institute, Ljubljana, Slovenia.
2013 (English)In: The 36th international convention on information and communication technology, electronics and microelectronics MIPRO 2013, 2013, Vol. Article number 6596280, 369-374 p.Conference paper, Published paper (Refereed)
Abstract [en]

Hadoop MapReduce has become one of the most popular tools for data processing. Hadoop is normally installed on a cluster of computers. When the cluster becomes undersized, it can be scaled by adding new computers and storage devices, but it can also be extended by real or virtual resources from another computer cluster. We present a utilization of the MapReduce paradigm on a Hadoop installation extended across two clusters connected over the Internet. We measured execution times of Map and Reduce tasks in a multicluster environment, and compared them to the corresponding times obtained while only computers from a single cluster are used. The results show that there might be a decrease inMapReduce performance depending on: the concrete data analyses application, the ratio of the number of local and remote computers, and connection bandwidth to remote computers. Additionally, the investigation suggests an upgrade to the Apache Hadoop MapReduce, making it more adjusted to the multicluster environment.

Place, publisher, year, edition, pages
2013. Vol. Article number 6596280, 369-374 p.
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-30489ISBN: 978-953-233-076-2 (print)OAI: oai:DiVA.org:mdh-30489DiVA: diva2:888134
Conference
The 36th international convention on information and communication technology, electronics and microelectronics MIPRO 2013, 20-24 May 2013, Opatija, Croatia
Available from: 2015-12-22 Created: 2015-12-21 Last updated: 2015-12-22Bibliographically approved

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
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  • vancouver
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Language
  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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