Computation capability deduction architecture for MapReduce on cloud computing

Tzu Chi Huang, Kuo Chih Chu, Guo Hao Huang, Yan Chen Shen, Ce Kuen Shieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

MapReduce gradually becomes the de facto programming standard of applications on cloud computing. However, MapReduce needs a cloud administrator to manually configure parameters of the run-time system such as slot numbers for Map and Reduce tasks in order to get the best performance. Because the manual configuration has a risk of performance degradation, MapReduce should utilize the Computation Capability Deduction Architecture (CCDA) proposed in this paper to avoid the risk. MapReduce can use CCDA to help the run-time system to distribute appropriate numbers of tasks over computers in a cloud at run time without any manual configuration made by a cloud administrator. According to experiment observations in this paper, MapReduce can get great performance improvement with the help of CCDA in data-intensive applications such as Inverted Index and Word Count that are usually required to process big data on cloud computing.

Original languageEnglish
Title of host publicationProceedings - 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017
EditorsShi-Jinn Horng
PublisherIEEE Computer Society
Pages368-375
Number of pages8
ISBN (Electronic)9781538631515
DOIs
Publication statusPublished - 2018 Mar 27
Event18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017 - Taipei, Taiwan
Duration: 2017 Dec 182017 Dec 20

Publication series

NameParallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
Volume2017-December

Other

Other18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017
CountryTaiwan
CityTaipei
Period17-12-1817-12-20

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Theoretical Computer Science
  • Computer Science Applications

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  • Cite this

    Huang, T. C., Chu, K. C., Huang, G. H., Shen, Y. C., & Shieh, C. K. (2018). Computation capability deduction architecture for MapReduce on cloud computing. In S-J. Horng (Ed.), Proceedings - 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017 (pp. 368-375). (Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings; Vol. 2017-December). IEEE Computer Society. https://doi.org/10.1109/PDCAT.2017.00067