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Straggler handling approaches in mapreduce framework: a comparative study

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 11 No. 1 (2021)

DOI
10.11591/ijece.v11i1.pp375-382
ISSN
2722-2578, 2088-8708
Published
2021-02-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The proliferation of information technology produces a huge amount of data called big data that cannot be processed by traditional database systems. These Various types of data come from different sources. However, stragglers are a major bottleneck in big data processing, and hence the early detection and accurate identification of stragglers can have important impacts on the performance of big data processing. This work aims to assess five stragglers identification methods: Hadoop native scheduler, LATE Scheduler, Mantri, MonTool, and Dolly. The performance of these techniques was evaluated based on three benchmarked methods: Sort, Grep and WordCount. The results show that the LATE Scheduler performs the best and it would be efficient to obtain better results for stragglers identification.

Collaboration network of the authors

29 authors · 77 co-authorship links · 40 across different organisations

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  • Universiti Sains Malaysia
  • Universiti of Malaysia Sabah
  • Universiti Malaysia Perlis
  • Liwa University
  • Al-Ahliyya Amman University
  • Woosong University
  • Al-Aqsa University
  • 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
Mosleh M. AbualhajAl-Ahliyya Amman University2
Alhamza AlalousiUniversiti Malaysia Perlis2
Rozmie Razif OthmanUniversiti Malaysia Perlis2
Shahrul NizamUniversiti Malaysia Perlis2
Yu‐Beng LeauUniversiti of Malaysia Sabah2
Ahamad Tajudin KhaderUniversiti Sains Malaysia1
Mohammed Azmi Al‐BetarAffiliation unknown1
Syibrah NaimWoosong University1
Mohammed A. AwadallahAl-Aqsa University1
Osama Ahmad Alomariİstanbul Gelişim Üniversitesi1
Zaid Abdi Alkareem AlyasseriNational University of Malaysia1
Adnan Hasan Bdair AIghuraibawiUniversiti Sains Malaysia1
Selvakumar ManickamUniversiti Sains Malaysia1
Low Xian WeeUniversiti of Malaysia Sabah1
Zhiwei YanChina Internet Network Information Center1
Yong Jin ParkUniversiti of Malaysia Sabah1
Arkan Hammoodi Hasan KablaUniversiti Sains Malaysia1
Shady HamoudaLiwa University1
Abdullah Ahmed BahashwanUniversiti Sains Malaysia1
Taief Alaa Al-AmiedyUniversiti Sains Malaysia1
Iznan Husainy HasbullahUniversiti Sains Malaysia1
Serri FaisalLiwa University1
Mohammad Shahrul Mohd ShahUniversiti of Malaysia Sabah1
Liang ZhaoShenyang Aerospace University1

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>Straggler handling approaches in mapreduce framework: a comparative study</dc:title><dc:creator>Katrawi, Anwar H.</dc:creator><dc:creator>Abdullah, Rosni</dc:creator><dc:creator>Anbar, Mohammed</dc:creator><dc:creator>AlShourbaji, Ibrahim</dc:creator><dc:creator>Abasi, Ammar Kamal</dc:creator><dc:description>&lt;jats:p&gt;The proliferation of information technology produces a huge amount of data called big data that cannot be processed by traditional database systems. These Various types of data come from different sources. However, stragglers are a major bottleneck in big data processing, and hence the early detection and accurate identification of stragglers can have important impacts on the performance of big data processing. This work aims to assess five stragglers identification methods: Hadoop native scheduler, LATE Scheduler, Mantri, MonTool, and Dolly. The performance of these techniques was evaluated based on three benchmarked methods: Sort, Grep and WordCount. The results show that the LATE Scheduler performs the best and it would be efficient to obtain better results for stragglers identification.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2021-02-01</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.v11i1.pp375-382</dc:identifier><dc:identifier>10.11591/ijece.v11i1.pp375-382</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 11 No 1 (2021); 375</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/22595/14495</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>