Straggler mitigation in hadoop mapreduce framework: a review

• Main Authors: Ajibade, Lukuman Saheed, Abu Bakar, Kamalrulnizam, Aliyu, Ahmed
• Format: Article
• Language: English
• Published: Science and Information Organization 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/98703/1/LukumanSaheedAjibade2022_StragglerMitigationinHadoopMapReduce.pdf; http://eprints.utm.my/id/eprint/98703/; http://dx.doi.org/10.14569/IJACSA.2022.01308101
• Institution: Universiti Teknologi Malaysia

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.