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.
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||International Workshops on Data Mining and Decision Analytics for Public Health, Biologically Inspired Data Mining Techniques, Mobile Data Management, Mining, and Computing on Social Networks, Big Data Science and Engineering on E-Commerce, Cloud Service Discovery, MSMV-MBI, Scalable Dats Analytics, Data Mining and Decision Analytics for Public Health and Wellness, Algorithms for Large-Scale Information Processing in Knowledge Discovery, Data Mining in Social Networks, Data Mining in Biomedical informatics and Healthcare, Pattern Mining and Application of Big Data in conjunction with 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2014|
|Period||14-05-13 → 14-05-16|
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)