MapReduce: an infrastructure review and research insights

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 processi...

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Bibliographic Details
Published in:The Journal of supercomputing Vol. 75; no. 10; pp. 6934 - 7002
Main Authors: Maleki, Neda, Rahmani, Amir Masoud, Conti, Mauro
Format: Journal Article
Language:English
Published: New York Springer US 01.10.2019
Springer Nature B.V
Subjects:
Compilers
Computer Science
Data search
Fault tolerance
Infrastructure
Interpreters
Parallel processing
Parallel programming
Processor Architectures
Programming Languages
Provisioning
Resource allocation
Task scheduling
Parallel and distributed programming model
Systematic review
MapReduce paradigm
Hadoop
ISSN:0920-8542, 1573-0484
Online Access:Get full text
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Summary: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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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-019-02907-5