期刊
ENTROPY
卷 21, 期 9, 页码 -出版社
MDPI
DOI: 10.3390/e21090902
关键词
big data; database design; entropy; information transfer; social networks; information security; scheduling; task volume; classification; optimization
资金
- Natural Science Foundation of Inner Mongolia [2018MS6010]
- Foundation Science Research Start-up Fund of Inner Mongolia Agriculture University [JC2016005]
- Scientific Research Foundation for Doctors of Inner Mongolia Agriculture University [NDYB2016-11]
In social network big data scheduling, it is easy for target data to conflict in the same data node. Of the different kinds of entropy measures, this paper focuses on the optimization of target entropy. Therefore, this paper presents an optimized method for the scheduling of big data in social networks and also takes into account each task's amount of data communication during target data transmission to construct a big data scheduling model. Firstly, the task scheduling model is constructed to solve the problem of conflicting target data in the same data node. Next, the necessary conditions for the scheduling of tasks are analyzed. Then, the a periodic task distribution function is calculated. Finally, tasks are scheduled based on the minimum product of the corresponding resource level and the minimum execution time of each task is calculated. Experimental results show that our optimized scheduling model quickly optimizes the scheduling of social network data and solves the problem of strong data collision.
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