MapReduce Job Optimization: A Mapping Study

Qinghua Lu; Liming Zhu; He Zhang; Dongyao Wu; Zheng Li; Xiwei Xu

doi:10.1109/CCBD.2015.33

MapReduce Job Optimization: A Mapping Study

Qinghua Lu, Liming Zhu, He Zhang, Dongyao Wu, Zheng Li, Xiwei Xu

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

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.

Originalspråk	engelska
Titel på värdpublikation	Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015
Förlag	IEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor	81-88
Antal sidor	8
ISBN (elektroniskt)	9781467383509
DOI	https://doi.org/10.1109/CCBD.2015.33
Status	Published - 2016 apr. 8
Evenemang	International Conference on Cloud Computing and Big Data, CCBD 2015 - Shanghai, Kina Varaktighet: 2015 nov. 4 → 2015 nov. 6

Konferens

Konferens	International Conference on Cloud Computing and Big Data, CCBD 2015
Land/Territorium	Kina
Ort	Shanghai
Period	2015/11/04 → 2015/11/06

Ämnesklassifikation (UKÄ)

Systemvetenskap, informationssystem och informatik

Tillgång till dokument

10.1109/CCBD.2015.33

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Link to publication in Scopus

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title = "MapReduce Job Optimization: A Mapping Study",

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 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.",

keywords = "big data, job optimization, mapping study, MapReduce, systematic literature review",

author = "Qinghua Lu and Liming Zhu and He Zhang and Dongyao Wu and Zheng Li and Xiwei Xu",

year = "2016",

month = apr,

day = "8",

doi = "10.1109/CCBD.2015.33",

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pages = "81--88",

booktitle = "Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "International Conference on Cloud Computing and Big Data, CCBD 2015 ; Conference date: 04-11-2015 Through 06-11-2015",

}

Lu, Q, Zhu, L, Zhang, H, Wu, D, Li, Z & Xu, X 2016, MapReduce Job Optimization: A Mapping Study. i Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015., 7450534, IEEE - Institute of Electrical and Electronics Engineers Inc., s. 81-88, International Conference on Cloud Computing and Big Data, CCBD 2015, Shanghai, Kina, 2015/11/04. https://doi.org/10.1109/CCBD.2015.33

MapReduce Job Optimization: A Mapping Study. / Lu, Qinghua; Zhu, Liming; Zhang, He et al.
Proceedings - 2015 International Conference on Cloud Computing and Big Data, CCBD 2015. IEEE - Institute of Electrical and Electronics Engineers Inc., 2016. s. 81-88 7450534.

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

TY - GEN

T1 - MapReduce Job Optimization

T2 - International Conference on Cloud Computing and Big Data, CCBD 2015

AU - Lu, Qinghua

AU - Zhu, Liming

AU - Zhang, He

AU - Wu, Dongyao

AU - Li, Zheng

AU - Xu, Xiwei

PY - 2016/4/8

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AB - 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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KW - job optimization

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KW - systematic literature review

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PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

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MapReduce Job Optimization: A Mapping Study

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