Automatic Self-Suspended Task for a MapReduce System on Cloud Computing

被引：1
作者：
Huang, Tzu-Chi [1 ]
Shieh, Ce-Kuen [2 ]
Huang, Sheng-Wei [2 ]
Chiu, Chui-Ming [2 ]
Liang, Tyng-Yeu [3 ]
机构：
[1] Lunghwa Univ Sci & Technol, Dept Elect Engn, Guishan, Taoyuan County, Taiwan
[2] Natl Cheng Kung Univ, Inst Comp & Commun Engn, Dept Elect Engn, Tainan, Taiwan
[3] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, Kaohsiung 807, Taiwan
来源：
TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING | 2014年 / 8643卷
关键词：
MapReduce; Cloud computing; ASST; Automatic Self-Suspended Task;
D O I：
10.1007/978-3-319-13186-3_25
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A MapReduce system gradually becomes an essential technology to achieve the large scale computing on cloud computing. A MapReduce system currently is designed to distribute tasks over nodes in a cloud according to manual configurations of slot numbers in nodes. However, a MapReduce system may have the performance degradation due to the inappropriate configuration of the slot number, because the slot number can not exactly reflect the performance of the node. A MapReduce system can utilize the Automatic Self-Suspended Task (ASST) proposed in this paper to alleviate the performance degradation due to the inappropriate configuration of the slot number in a node on cloud computing. In experiments of this paper, a MapReduce system is proved to have a better performance with the help of ASST for various applications on cloud computing.
引用
收藏
页码：257 / 268
页数：12
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