Adaptive Combiner for MapReduce on cloud computing

被引:6
作者
Huang, Tzu-Chi [1 ]
Chu, Kuo-Chih [1 ]
Lee, Wei-Tsong [2 ]
Ho, Yu-Sheng [2 ]
机构
[1] Lunghwa Univ Sci & Technol, Dept Elect Engn, Taoyuan, Taiwan
[2] Tamkang Univ, Dept Elect Engn, Taipei, Taiwan
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2014年 / 17卷 / 04期
关键词
MapReduce; Combiner; Cloud computing; ACMR;
D O I
10.1007/s10586-014-0362-3
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
MapReduce is a programming model to process a massive amount of data on cloud computing. MapReduce processes data in two phases and needs to transfer intermediate data among computers between phases. MapReduce allows programmers to aggregate intermediate data with a function named combiner before transferring it. By leaving programmers the choice of using a combiner, MapReduce has a risk of performance degradation because aggregating intermediate data benefits some applications but harms others. Now, MapReduce can work with our proposal named the Adaptive Combiner for MapReduce (ACMR) to automatically, smartly, and trainer for getting a better performance without any interference of programmers. In experiments on seven applications, MapReduce can utilize ACMR to get the performance comparable to the system that is optimal for an application.
引用
收藏
页码:1231 / 1252
页数:22
相关论文
共 34 条
[1]  
AiLing Duan, 2012, 2012 International Conference on Systems and Informatics (ICSAI 2012), P2462, DOI 10.1109/ICSAI.2012.6223552
[2]  
[Anonymous], 2009, Hadoop: The Definitive Guide
[3]  
Astrachan O., 2003, SIGCSE Bulletin, V35, P1, DOI 10.1145/792548.611918
[4]   Semantic grep: Regular expressions plus relational abstraction [J].
Bull, RI ;
Trevors, A ;
Malton, AJ ;
Godfrey, MW .
NINTH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, 2002, :267-276
[5]  
Condie T., 2010, P 7 USENIX C NETW SY, P12
[6]  
Dean J, 2004, USENIX ASSOCIATION PROCEEDINGS OF THE SIXTH SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION (OSDE '04), P137
[7]  
Ekanayake J, 2010, P 19 ACM INT S HIGH, P810, DOI [DOI 10.1145/1851476.1851593, 10.1145/1851476.1851593]
[8]  
Fadika Z., 2011, GRID COMPUTING IEEEA, P90
[9]  
Gaizhen Yang, 2011, 2011 2nd International Symposium on Intelligence Information Processing and Trusted Computing, P154, DOI 10.1109/IPTC.2011.46
[10]   EXPONENTIAL SMOOTHING - THE STATE OF THE ART [J].
GARDNER, ES .
JOURNAL OF FORECASTING, 1985, 4 (01) :1-28