Computation capability deduction architecture for MapReduce on cloud computing

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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings - 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017
編輯Shi-Jinn Horng
發行者IEEE Computer Society
頁面368-375
頁數8
ISBN(電子)9781538631515
DOIs
出版狀態Published - 2018 三月 27
事件18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017 - Taipei, Taiwan
持續時間: 2017 十二月 182017 十二月 20

出版系列

名字Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
2017-December

Other

Other18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017
國家/地區Taiwan
城市Taipei
期間17-12-1817-12-20

All Science Journal Classification (ASJC) codes

  • 軟體
  • 硬體和架構
  • 理論電腦科學
  • 電腦科學應用

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