@inproceedings{262749ad3be342099ff7ea888ee3bd09,
title = "Computation capability deduction architecture for MapReduce on cloud computing",
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.",
author = "Huang, {Tzu Chi} and Chu, {Kuo Chih} and Huang, {Guo Hao} and Shen, {Yan Chen} and Shieh, {Ce Kuen}",
note = "Funding Information: ACKNOWLEDGMENT We thank the Taiwan Ministry of Science and Technology for the supports of this research project under grant number MOST 106-2221-E-262-004. Besides, we want to thank Lunghwa University of Science and Technology for providing us with various devices. We further offer our special thanks to the reviewers for their valuable comments and suggestions.; 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017 ; Conference date: 18-12-2017 Through 20-12-2017",
year = "2018",
month = mar,
day = "27",
doi = "10.1109/PDCAT.2017.00067",
language = "English",
series = "Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings",
publisher = "IEEE Computer Society",
pages = "368--375",
editor = "Shi-Jinn Horng",
booktitle = "Proceedings - 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2017",
address = "United States",
}