An extreme learning approach to fast prediction in the reduce phase of a cloud platform

Liu, Q, Cai, W, Shen, J, Wang, B, Fu, Z and Linge, N ORCID: https://orcid.org/0000-0002-4318-8782 2016, An extreme learning approach to fast prediction in the reduce phase of a cloud platform , in: International Conference on Cloud Computing and Security (ICCCS 2015), 13th-15th August 2015, Nanjing, China.

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Abstract

As a widely used programming model for the purposes of processing large data sets, MapReduce (MR) becomes inevitable in data clusters or grids, e.g. a Hadoop environment. However, experienced programmers are needed to decide the number of reducers used during the reduce phase of the MR, which makes the quality of MR scripts differ. In this paper, an extreme learning method is employed to recommend potential number of reducer a mapped task needs. Execution time is also predicted for user to better arrange their tasks. According to the results, our method can provide fast prediction than SVM with similar accuracy maintained.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer
Series Name: Lecture Notes in Computer Science
ISBN: 9783319270500; 9783319270517
ISSN: 0302-9743
Related URLs:
Depositing User: USIR Admin
Date Deposited: 29 Nov 2016 15:32
Last Modified: 17 Aug 2020 13:40
URI: http://usir.salford.ac.uk/id/eprint/40924

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