Advanced

MapReduce Job Optimization : A Mapping Study

Lu, Qinghua; Zhu, Liming; Zhang, He; Wu, Dongyao; Li, Zheng LU and Xu, Xiwei (2016) International Conference on Cloud Computing and Big Data, CCBD 2015 In Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015 p.81-88
Abstract

MapReduce has become the standard model for supporting big data analytics. In particular, MapReduce job optimization has been widely considered to be crucial in the implementations of big data analytics. However, there is still a lack of guidelines especially for practitioners to understand how the MapReduce jobs can be optimized. This paper aims to systematic identify and taxonomically classify the existing work on job optimization. We conducted a mapping study on 47 selected papers that were published between 2004 and 2014. We classified and compared the selected papers based on a 5WH-based characterization framework. This study generates a knowledge base of current job optimization solutions and also identifies a set of research gaps... (More)

MapReduce has become the standard model for supporting big data analytics. In particular, MapReduce job optimization has been widely considered to be crucial in the implementations of big data analytics. However, there is still a lack of guidelines especially for practitioners to understand how the MapReduce jobs can be optimized. This paper aims to systematic identify and taxonomically classify the existing work on job optimization. We conducted a mapping study on 47 selected papers that were published between 2004 and 2014. We classified and compared the selected papers based on a 5WH-based characterization framework. This study generates a knowledge base of current job optimization solutions and also identifies a set of research gaps and opportunities. This study concludes that job optimization is still in an early stage of maturity. More attentions need to be paid to the cross-data center, cluster or rack job optimization to improve communication efficiency.

(Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
big data, job optimization, mapping study, MapReduce, systematic literature review
in
Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015
pages
8 pages
publisher
Institute of Electrical and Electronics Engineers Inc.
conference name
International Conference on Cloud Computing and Big Data, CCBD 2015
external identifiers
  • Scopus:84969506247
DOI
10.1109/CCBD.2015.33
language
English
LU publication?
yes
id
ef9c319d-6be8-4986-83ed-aa9971c7be51
date added to LUP
2016-10-03 14:30:22
date last changed
2016-10-11 14:48:17
@misc{ef9c319d-6be8-4986-83ed-aa9971c7be51,
  abstract     = {<p>MapReduce has become the standard model for supporting big data analytics. In particular, MapReduce job optimization has been widely considered to be crucial in the implementations of big data analytics. However, there is still a lack of guidelines especially for practitioners to understand how the MapReduce jobs can be optimized. This paper aims to systematic identify and taxonomically classify the existing work on job optimization. We conducted a mapping study on 47 selected papers that were published between 2004 and 2014. We classified and compared the selected papers based on a 5WH-based characterization framework. This study generates a knowledge base of current job optimization solutions and also identifies a set of research gaps and opportunities. This study concludes that job optimization is still in an early stage of maturity. More attentions need to be paid to the cross-data center, cluster or rack job optimization to improve communication efficiency.</p>},
  author       = {Lu, Qinghua and Zhu, Liming and Zhang, He and Wu, Dongyao and Li, Zheng and Xu, Xiwei},
  keyword      = {big data,job optimization,mapping study,MapReduce,systematic literature review},
  language     = {eng},
  month        = {04},
  pages        = {81--88},
  publisher    = {ARRAY(0x831a998)},
  series       = {Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015},
  title        = {MapReduce Job Optimization : A Mapping Study},
  url          = {http://dx.doi.org/10.1109/CCBD.2015.33},
  year         = {2016},
}