Data analysis on big data: improving the map and shuffle phases in Hadoop Map Reduce

Lakshmi, J.V.N.

doi:10.1504/ijdats.2018.094130

IJDATS

2018

DOI: 10.1504/ijdats.2018.094130

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Data analysis on big data: improving the map and shuffle phases in Hadoop Map Reduce

J.V.N. Lakshmi

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Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review

Saheed¹,

Kamalrulnizam

Aliyu

et al. 2021

Sys Lit Rev & Meta-Anl J

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Processing huge and complex data to obtain useful information is challenging, even though several big data processing frameworks have been proposed and further enhanced. One of the prominent big data processing frameworks is MapReduce. The main concept of MapReduce framework relies on distributed and parallel processing. However, MapReduce framework is facing serious performance degradations due to the slow execution of certain tasks type called stragglers. Failing to handle stragglers causes delay and affects the overall job execution time. Meanwhile, several straggler reduction techniques have been proposed to improve the MapReduce performance. This study provides a comprehensive and qualitative review of the different existing straggler mitigation solutions. In addition, a taxonomy of the available straggler mitigation solutions is presented. Critical research issues and future research directions are identified and discussed to guide researchers and scholars

show abstract

Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review

Saheed¹,

Kamalrulnizam

Aliyu

et al. 2021

Sys Lit Rev & Meta-Anl J

View full text Add to dashboard Cite

show abstract

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Data analysis on big data: improving the map and shuffle phases in Hadoop Map Reduce

Cited by 1 publication

References 0 publications

Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review

Latency-aware Straggler Mitigation Strategy in Hadoop MapReduce Framework: A Review

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