Title |
A genetic algorithm-based job scheduling model for big data analytics
|
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Published in |
EURASIP Journal on Wireless Communications and Networking, June 2016
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DOI | 10.1186/s13638-016-0651-z |
Pubmed ID | |
Authors |
Qinghua Lu, Shanshan Li, Weishan Zhang, Lei Zhang |
Abstract |
Big data analytics (BDA) applications are a new category of software applications that process large amounts of data using scalable parallel processing infrastructure to obtain hidden value. Hadoop is the most mature open-source big data analytics framework, which implements the MapReduce programming model to process big data with MapReduce jobs. Big data analytics jobs are often continuous and not mutually separated. The existing work mainly focuses on executing jobs in sequence, which are often inefficient and consume high energy. In this paper, we propose a genetic algorithm-based job scheduling model for big data analytics applications to improve the efficiency of big data analytics. To implement the job scheduling model, we leverage an estimation module to predict the performance of clusters when executing analytics jobs. We have evaluated the proposed job scheduling model in terms of feasibility and accuracy. |
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Canada | 1 | 33% |
Unknown | 2 | 67% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Malaysia | 1 | 1% |
United States | 1 | 1% |
Unknown | 75 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 31 | 40% |
Student > Ph. D. Student | 11 | 14% |
Researcher | 6 | 8% |
Lecturer | 5 | 6% |
Student > Bachelor | 4 | 5% |
Other | 11 | 14% |
Unknown | 9 | 12% |
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Computer Science | 29 | 38% |
Social Sciences | 13 | 17% |
Arts and Humanities | 7 | 9% |
Engineering | 6 | 8% |
Business, Management and Accounting | 4 | 5% |
Other | 7 | 9% |
Unknown | 11 | 14% |