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A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 8 No. 3 (2018)

DOI
10.11591/ijece.v8i3.pp1854-1862
ISSN
2088-8708, 2088-8708
Published
2018-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement.

Collaboration network of the authors

39 authors · 134 co-authorship links · 63 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Multimedia University
  • Universiti Putra Malaysia
  • Alliance University
  • Pabna University of Science and Technology
  • University of Technology Malaysia
  • Feni University
  • Tun Hussein Onn University of Malaysia
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Abdullah Al MamunMultimedia University8
Joseph Emerson RajaMultimedia University4
Gajula Ramana MurthyAlliance University4
Chy. Mohammed Tawsif KhanMultimedia University4
Jaya GanesanAlliance University4
Poh Ping EmMultimedia University3
Anik TahabilderWayne State University3
Md Shohel SayeedMultimedia University3
Nor Azlina Ab. AzizMultimedia University3
Azlan Abd AzizMultimedia University3
Ahmmad MushaPabna University of Science and Technology2
E.M.H. ArifAffiliation unknown2
Tharek Abd RahmanUniversity of Technology Malaysia2
Omar Abdul AzizUniversity of Technology Malaysia2
Mohd Nazeri KamaruddinMultimedia University2
Azlan Abdul AzizAffiliation unknown2
Mina AsaduzamanAffiliation unknown2
Busrat HossenFeni University1
Sima RanjbariTarbiat Modares University1
Rehnuma HasnatPabna University of Science and Technology1
Sohag Kumar SahaTennessee Technological University1
Busrat JahanFeni University1
Ali Afzalian MandMultimedia University1
Muhammad Amer Ridzuan Bin ZuberMultimedia University1
Fazirulhisyam HashimUniversiti Putra Malaysia1
Nnamdi Henry UmeloUniversiti Putra Malaysia1
Nor K. NoordinUniversiti Putra Malaysia1
Mohd Fadlee A. RasidUniversiti Putra Malaysia1
Yaser Awadh BakhuraisaMultimedia University1
Saifulnizan JamianTun Hussein Onn University of Malaysia1
Fajaruddin MustakimTun Hussein Onn University of Malaysia1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance</dc:title><dc:creator>Rahman, Md. Armanur</dc:creator><dc:creator>Hossen, J.</dc:creator><dc:creator>C, Venkataseshaiah</dc:creator><dc:creator>Ho, CK</dc:creator><dc:creator>Geok, Tan Kim</dc:creator><dc:creator>Sultana, Aziza</dc:creator><dc:creator>H., Jesmeen M. Z.</dc:creator><dc:creator>Hossain, Ferdous</dc:creator><dc:description>&lt;jats:p&gt;The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2018-06-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v8i3.pp1854-1862</dc:identifier><dc:identifier>10.11591/ijece.v8i3.pp1854-1862</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 8 No 3 (2018); 1854</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/11793/8726</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>