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2019 Volume 34
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Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization

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  • Abstract: Recently, valuable knowledge that can be retrieved from a huge volume of datasets (called Big Data) set in motion the development of frameworks to process data based on parallel and distributed computing, including Apache Hadoop, Facebook Corona, and Microsoft Dryad. Apache Hadoop is an open source implementation of Google MapReduce that attracted strong attention from the research community both in academia and industry. Hadoop MapReduce scheduling algorithms play a critical role in the management of large commodity clusters, controlling QoS requirements by supervising users, jobs, and tasks execution. Hadoop MapReduce comprises three schedulers: FIFO, Fair, and Capacity. However, the research community has developed new optimizations to consider advances and dynamic changes in hardware and operating environments. Numerous efforts have been made in the literature to address issues of network congestion, straggling, data locality, heterogeneity, resource under-utilization, and skew mitigation in Hadoop scheduling. Recently, the volume of research published in journals and conferences about Hadoop scheduling has consistently increased, which makes it difficult for researchers to grasp the overall view of research and areas that require further investigation. A scientific literature review has been conducted in this study to assess preceding research contributions to the Apache Hadoop scheduling mechanism. We classify and quantify the main issues addressed in the literature based on their jargon and areas addressed. Moreover, we explain and discuss the various challenges and open issue aspects in Hadoop scheduling optimizations.
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  • Cite this article

    Muhammad Hanif, Choonhwa Lee. 2019. Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization. The Knowledge Engineering Review. 34:371 doi: 10.1017/S0269888918000371
    Muhammad Hanif, Choonhwa Lee. 2019. Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization. The Knowledge Engineering Review. 34:371 doi: 10.1017/S0269888918000371
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Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization

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The Knowledge Engineering Review  34,  Article number: e4  (2019)  |  Cite this article

Abstract: Abstract: Recently, valuable knowledge that can be retrieved from a huge volume of datasets (called Big Data) set in motion the development of frameworks to process data based on parallel and distributed computing, including Apache Hadoop, Facebook Corona, and Microsoft Dryad. Apache Hadoop is an open source implementation of Google MapReduce that attracted strong attention from the research community both in academia and industry. Hadoop MapReduce scheduling algorithms play a critical role in the management of large commodity clusters, controlling QoS requirements by supervising users, jobs, and tasks execution. Hadoop MapReduce comprises three schedulers: FIFO, Fair, and Capacity. However, the research community has developed new optimizations to consider advances and dynamic changes in hardware and operating environments. Numerous efforts have been made in the literature to address issues of network congestion, straggling, data locality, heterogeneity, resource under-utilization, and skew mitigation in Hadoop scheduling. Recently, the volume of research published in journals and conferences about Hadoop scheduling has consistently increased, which makes it difficult for researchers to grasp the overall view of research and areas that require further investigation. A scientific literature review has been conducted in this study to assess preceding research contributions to the Apache Hadoop scheduling mechanism. We classify and quantify the main issues addressed in the literature based on their jargon and areas addressed. Moreover, we explain and discuss the various challenges and open issue aspects in Hadoop scheduling optimizations.

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    • This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (No. 2017R1A2B4010395).

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    Muhammad Hanif, Choonhwa Lee. 2019. Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization. The Knowledge Engineering Review. 34:371 doi: 10.1017/S0269888918000371
    Muhammad Hanif, Choonhwa Lee. 2019. Jargon of Hadoop MapReduce scheduling techniques: a scientific categorization. The Knowledge Engineering Review. 34:371 doi: 10.1017/S0269888918000371
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