MapReduce Job Optimization : A Mapping Study
(2016) 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.
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- author
- Lu, Qinghua ; Zhu, Liming ; Zhang, He ; Wu, Dongyao ; Li, Zheng LU and Xu, Xiwei
- organization
- publishing date
- 2016-04-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- big data, job optimization, mapping study, MapReduce, systematic literature review
- host publication
- Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015
- article number
- 7450534
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- International Conference on Cloud Computing and Big Data, CCBD 2015
- conference location
- Shanghai, China
- conference dates
- 2015-11-04 - 2015-11-06
- external identifiers
-
- wos:000380552500012
- scopus:84969506247
- ISBN
- 9781467383509
- 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
- 2022-01-30 06:29:39
@inproceedings{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}},
booktitle = {{Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015}},
isbn = {{9781467383509}},
keywords = {{big data; job optimization; mapping study; MapReduce; systematic literature review}},
language = {{eng}},
month = {{04}},
pages = {{81--88}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{MapReduce Job Optimization : A Mapping Study}},
url = {{http://dx.doi.org/10.1109/CCBD.2015.33}},
doi = {{10.1109/CCBD.2015.33}},
year = {{2016}},
}