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Earlier stage for straggler detection and handling using combined CPU test and LATE methodology

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 10 No. 5 (2020)

DOI
10.11591/ijece.v10i5.pp4910-4917
ISSN
2722-2578, 2088-8708
Published
2020-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

Using MapReduce in Hadoop helps in lowering the execution time and power consumption for large scale data. However, there can be a delay in job processing in circumstances where tasks are assigned to bad or congested machines called "straggler tasks"; which increases the time, power consumptions and therefore increasing the costs and leading to a poor performance of computing systems. This research proposes a hybrid MapReduce framework referred to as the combinatory late-machine (CLM) framework. Implementation of this framework will facilitate early and timely detection and identification of stragglers thereby facilitating prompt appropriate and effective actions.

Collaboration network of the authors

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

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

  • 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
Ibrahim AlShourbajiAffiliation unknown4
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>Earlier stage for straggler detection and handling using combined CPU test and LATE methodology</dc:title><dc:creator>Katrawi, Anwar H.</dc:creator><dc:creator>Abdullah, Rosni</dc:creator><dc:creator>Anbar, Mohammed</dc:creator><dc:creator>Abasi, Ammar Kamal</dc:creator><dc:description>&lt;jats:p&gt;Using MapReduce in Hadoop helps in lowering the execution time and power consumption for large scale data. However, there can be a delay in job processing in circumstances where tasks are assigned to bad or congested machines called "straggler tasks"; which increases the time, power consumptions and therefore increasing the costs and leading to a poor performance of computing systems. This research proposes a hybrid MapReduce framework referred to as the combinatory late-machine (CLM) framework. Implementation of this framework will facilitate early and timely detection and identification of stragglers thereby facilitating prompt appropriate and effective actions.&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2020-10-01</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.v10i5.pp4910-4917</dc:identifier><dc:identifier>10.11591/ijece.v10i5.pp4910-4917</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 10 No 5 (2020); 4910</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/21201/14205</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>