# Dynamic Network Resource Autonomy Management and Task Scheduling Method
## 4 Simulation Experiments
### Numerical Comparative Analysis  
In our study, we define the evaluation metric of the algorithm as the task revenue [43], and a larger value of this value indicates a better algorithm effect. The value is calculated as  
\[M(X_{sub})\triangleq T(t)/p_{i}(t)\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration Items** & **Computer Configuration Information** \\ \hline Operating System & Windows 10 Family Chinese Version \\ CPU & Intel(R) Core(TM) i5-1035G1 CPU @ 1.00 GHz \\ Memory & 8 GB \\ Hard Disk & 512 GB \\ Video Cards & NVIDIA GeForce MX350 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Introduction to running environment configuration. Figure 8: Two-dimensional trajectory of the satellite. The algorithm for comparison with our proposed algorithm is as follows: Machine learning methods include DS (Dual Simplex Algorithm, DS) [44], PS (Primal Simplex Algorithm) [45], NS (Network Simplex Algorithm) [46], Barrier(Barrier Algorithm) [47] and Sifting(Sifting Algorithm) [48], all of which are based on the power of the solver CPLEX. In Table 2, in the running time comparison results table between different algorithms, the horizontal axis indicates various satellite scheduling algorithms, and the vertical axis shows the task dataset. The vertical axis from top to bottom suggests increasing the task dataset, i.e., data volume. The data in the table indicates the time various algorithms take to complete the task. It can be seen that when the data set is of the same standard, our proposed DUNM algorithm has a significant improvement in the index of time to complete the scheduling task compared with other machines' learning-based PS [45], DS [44], NS, Barrier [47], and Sifting algorithms, especially when the data set is smaller, e.g.,  
When the data set is D1, the DUNM running time is. For example, when the data set is D1, the DUNM running time is 0.465873, PS is 0.813586, DS is 0.841538, etc. Our proposed algorithm reduces the time to complete the scheduling task by nearly 40% compared with other algorithms. As the data set increases, our proposed algorithm reduces the time to complete the scheduling task compared with different algorithms. For example, in data sets D2, D3, and D4, the time taken by DUNM to complete the scheduling task is less than the rest of the algorithms. As shown in Figure 9, according to the line graph, we can more intuitively find that our proposed algorithm is shorter in running time as the dataset grows. The reason for this gap is that, on the one hand, since these algorithms are based on machine learning, a lot of time is needed for model training before testing the models. There is also data to process to generate more task spend and overhead. For example, in the case of the DS algorithm, if the amount of data for the satellite schedule is large, the algorithm may need more time to process it. The Primal-Simplex (PS) algorithm is similar to the DS algorithm because it is a mathematical optimization algorithm with high computational complexity, so it may take more time to complete the scheduling task. The remaining algorithms, such as the NS algorithm, Barrier algorithm/citezhang2018 breach, and Sifting algorithm, are similar to the above algorithms because they have high computational complexity. They are less efficient compared to our proposed algorithm, resulting in a longer time required to complete the task. While machine learning-based algorithms usually need to train the model first and then perform a lot of computation when predicting the task, our approach relies more on real-time data and some state information of the system. It does not need to train the model in advance. Linear programming-based algorithms require longer computation time in large-scale linear programming problems and may be affected by the data size. Our proposed algorithm focuses more on the dynamic allocation and scheduling of resources and is suitable for task-scheduling problems in dynamic scenarios.