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MapReduce: an infrastructure review and research insights

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Abstract

In the current decade, doing the search on massive data to find “hidden” and valuable information within it is growing. This search can result in heavy processing on considerable data, leading to the development of solutions to process such huge information based on distributed and parallel processing. Among all the parallel programming models, one that gains a lot of popularity is MapReduce. The goal of this paper is to survey researches conducted on the MapReduce framework in the context of its open-source implementation, Hadoop, in order to summarize and report the wide topic area at the infrastructure level. We managed to do a systematic review based on the prevalent topics dealing with MapReduce in seven areas: (1) performance; (2) job/task scheduling; (3) load balancing; (4) resource provisioning; (5) fault tolerance in terms of availability and reliability; (6) security; and (7) energy efficiency. We run our study by doing a quantitative and qualitative evaluation of the research publications’ trend which is published between January 1, 2014, and November 1, 2017. Since the MapReduce is a challenge-prone area for researchers who fall off to work and extend with, this work is a useful guideline for getting feedback and starting research.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Neda Maleki & Amir Masoud Rahmani

  2. Department of Mathematics, University of Padua, Padua, Italy

    Mauro Conti

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  1. Neda Maleki
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  2. Amir Masoud Rahmani
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  3. Mauro Conti
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Maleki, N., Rahmani, A.M. & Conti, M. MapReduce: an infrastructure review and research insights. J Supercomput 75, 6934–7002 (2019). https://doi.org/10.1007/s11227-019-02907-5

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