- freely available
Entropy 2019, 21(9), 902; https://doi.org/10.3390/e21090902
2. Entropy in Social Networks
3. Material Method
3.1. Scheduling Model
3.1.1. Partition Calculation for Scheduling Task Volume
3.1.2. Construction of Massive Data Scheduling Model for Social Networks
3.2. Optimized Implementation of Massive Data Scheduling Model in Social Networks
3.2.1. Analysis of Schedulable Conditions for Massive Data Classification Tasks in Social Networks
3.2.2. Big Data Classification and Optimization Scheduling Method in Social Networks
- In classification optimization of big data scheduling, the optimal minimum time (min time) ( to ) is calculated by Equation (22)
- When classifying and optimizing big data, Equation (23) is used to obtain the two-dimensional array .
- When big data classification optimization scheduling is performed, is sorted to obtain a minimum .
- is dispatched to when big data classification optimization scheduling.
4.1. Throughput Analysis
4.2. Analysis of Transmission Efficiency
4.3. Performance Comparison
- Task response time for three methods. This can be understood as the reaction time when a massive data scheduling task in a social network starts and stops.
- Overall time taken for the task completion of three methods. This can be understood as the time it takes for big data scheduling optimization to start after the first task starts and the last task stops.
- Efficiency reduction ratio of three methods. This can be understood as a comparison of the response times and actual completion times of the three methods.
- Optimized social network resource usage rate. This can be understood as a comparison between the effective sharing of a social network resources and the maximum utilization after optimization of three methods.
- Set different amounts of target data, compare three methods of scheduling optimization with the actual number and analyze the comprehensiveness of scheduling optimization for three methods.
- Balance. This shows the balance of data nodes.
- Frequency normalized value. This is used to evaluate the stability of a scheduling optimization method. The smaller the numerical fluctuation, the stronger the stability of a scheduling optimization method.
- The three methods have a large difference in scheduling response time. The task response time of the proposed method increases with the number of tasks and the maximum response time is 20 ms. The maximum time-consuming response time of dynamic organization scheduling method and greedy algorithm-based scheduling method is 48 ms and 64 ms, respectively. The task response time of the proposed method is the shortest and tasks are executed quickly.
- The overall completion time of each task under the proposed method is less than 200 ms. Overall, tasks scheduled under the dynamic organization scheduling method and the greedy algorithm based scheduling method take more time as the number of tasks increases. Their task completion time also gradually increases and takes longer than with the proposed method. The overall completion time of tasks by the proposed method is the shortest of the three.
- The efficiency reduction ratio of the proposed method is lower than that of the dynamic organization scheduling method and the greedy algorithm based scheduling method. The proposed method exhibits the same efficiency and high stability throughout.
- The optimized social network resource usage rate under the proposed method is as high as 100%, which is greater than the resource usage rate of the dynamic organization scheduling method and the greedy algorithm-based scheduling method. This demonstrates that there is no redundant data in big data optimized by the proposed method and its availability is high.
- When the proposed method optimizes big data scheduling in social networks, the maximum difference between the target data volume and the actual data volume is one and its error is small. For the other two methods, the maximum difference between the target data volume and the actual data volume is greater than the proposed method. It can be seen that the optimal scheduling of the proposed method is more comprehensive.
- There is a big gap in the balance of big data in social networks after the three methods are used. The balance of big data in a social network after scheduling optimization by the proposed method is as high as 0.99, indicating that the probability of conflict between data is only 0.01. After scheduling optimization by the other two methods, the balance of big data in social networks is not more than 0.6, the conflict between data is large and the scheduling optimization process is hindered.
- The frequency of the scheduling model optimized by the proposed method is between 0.5 and 0.7 and its fluctuation is small. It has the advantage of high stability compared with the other two methods.
Conflicts of Interest
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|Task Ordinal Oumber||Bandwidth/(Mb/s)|
|Path A||Path B||Path C|
|Task Ordinal Number||Path A||Path B||Path C|
|Before Adopting This Method||After Adopting the Method Presented in This Paper||Before Adopting This Method||After Adopting the Method Presented in This Paper||Before Adopting This Method||After Adopting the Method Presented in This Paper|
Based on Greedy Algorithm
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