Application of Improved DBSCAN Clustering Algorithm in Task Scheduling of Cloud Computing

被引：0

作者：

Wang L.-Y. ^{[1
]}

Sun B. ^{[1
]}

Qin T. ^{[1
]}

机构：

[1] Information Security Center, Beijing University of Posts and Telecommunications, Beijing

来源：

Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2017年 / 40卷

关键词：

Cloud computing environ ment; Cluster; Task scheduling;

D O I：

10.13190/j.jbupt.2017.s.015

中图分类号：

学科分类号：

摘要：

Cloud scheduling strategy based on improved density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm was proposed to solve the problem of low efficiency of task scheduling in the implementation of cloud computing environment. Firstly, an improved DBSCAN clustering algorithm was used to cluster tasks. Secondly, the classified tasks were matched with classified resources to solve the low matching degree in resources and tasks. Experiments showed that the average execution time of tasks on the terminal was reduced by about 35.2% after clustering task, and the task scheduling time had also been significantly reduced. © 2017, Editorial Department of Journal of Beijing University of Posts and Telecommunications. All right reserved.

引用

页码：68 / 71

页数：3

共 3 条

[1] Armbrust M., Fox A., Griffith R., Et al., A view of cloud computing, Communications of the ACM, 53, 4, pp. 50-58, (2010)
[2] Meinel L., Findeisen M., Hes M., Et al., Automated real-time surveillance for ambient assisted living using anomnidirectional camera, 2004 IEEE International Conference on Consumer Electronics, pp. 396-399, (2014)
[3] Li X., Jiang S., Zhang Q., Et al., A dynamic density-based clustering algorithm appropriate to large -scale text processing, Acta Scientiarum Naturalium Universitatis Pekinensis, 49, 1, pp. 133-139, (2013)

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