Optimization of Big Data Scheduling in Social Networks
Citations Over TimeTop 1% of 2019 papers
Abstract
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.
Related Papers
- → A study on Multi-objective Flexible Job-shop Dynamic Scheduling Based on Control Window(2020)1 cited
- Status and Prospect of Network Scheduling Algorithm in Networked Control Systems(2008)
- Job scheduling in a distributed system(1983)