Next Article in Journal
A Formal Model for Semantic Computing Based on Generalized Probabilistic Automata
Next Article in Special Issue
TOPSIS Method for Probabilistic Linguistic MAGDM with Entropy Weight and Its Application to Supplier Selection of New Agricultural Machinery Products
Previous Article in Journal
What Is the Entropy of a Social Organization?
Previous Article in Special Issue
Does Classifier Fusion Improve the Overall Performance? Numerical Analysis of Data and Fusion Method Characteristics Influencing Classifier Fusion Performance
Open AccessArticle

Optimization of Big Data Scheduling in Social Networks

by Weina Fu 1,2, Shuai Liu 1,2,3 and Gautam Srivastava 4,5,*
1
College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
2
Hunan Provincial Key Laboratory of Intelligent Computer and Language Information Processing, Hunan Normal University, Changsha 410081, China
3
College of Computer Science, Inner Mongolia University, Hohhot 010012, China
4
Department of Mathematics and Computer Science, Brandon University, Brandon, MB R7A 6A9, Canada
5
Research Center for Interneural Computing, China Medical University, Taichung 40402, Taiwan
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(9), 902; https://doi.org/10.3390/e21090902
Received: 5 August 2019 / Revised: 3 September 2019 / Accepted: 10 September 2019 / Published: 17 September 2019
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. View Full-Text
Keywords: big data; database design; entropy; information transfer; social networks; information security; scheduling; task volume; classification; optimization big data; database design; entropy; information transfer; social networks; information security; scheduling; task volume; classification; optimization
Show Figures

Figure 1

MDPI and ACS Style

Fu, W.; Liu, S.; Srivastava, G. Optimization of Big Data Scheduling in Social Networks. Entropy 2019, 21, 902.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop