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

Anwar H. Katrawi, Rosni Abdullah, Mohammed Anbar, Ammar Kamal Abasi


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.


Big data; Combinatory late-machine; Hadoop; Mapreduce; Straggler

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DOI: http://doi.org/10.11591/ijece.v10i5.pp4910-4917

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578

This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).