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

Autor:	Tasneem Darwish, Ahmed Aliyu, Ajibade Lukuman Saheed, Abu Bakar Kamalrulnizam
Rok vydání:	2021
Předmět:	Complex data type Reduction (complexity) Big data processing Parallel processing (DSP implementation) business.industry Computer science Distributed computing Big data Speculative execution Latency (engineering) business Critical research
Zdroj:	Systematic Literature Review and Meta-Analysis Journal. 2:53-60
ISSN:	2753-9148 2753-913X
DOI:	10.54480/slrm.v2i2.19
Popis:	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
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0c15cdf5868ed00829c838dcf604931f https://doi.org/10.54480/slrm.v2i2.19 Zobrazit plný text záznamu