# Dynamic Network Resource Autonomy Management and Task Scheduling Method
## 4 Simulation Experiments
### Numerical Comparative Analysis
Comparing the revenue of completing a task, the revenue of DUNM is 1.23, 2.03, 1.58, and 2.00 in data sets D1, D2, D3, and D4, respectively; the PS algorithm is 0.963, 1.27, 2.73 and 1.96, respectively; the rest of the algorithms are similar to PS, and all have lower task revenue. All of our proposed algorithms have higher total revenue for the dataset scheduling task because we complete more scheduling tasks, so we spend more. By comparing the task revenue of completing a task, our algorithm is significantly more effective than other algorithms, and the more the number of tasks in the dataset, the more effective our algorithm becomes. This is sufficient to show the efficiency of our proposed algorithm. The probability of successfully assigned and unsuccessful tasks, which is an important indicator of the effectiveness of the scheduling algorithm, is 100% for DUNM. In contrast, the probability of successfully assigned tasks decreases with the dataset increase for the rest of the algorithms. Even when the dataset is D4, the probability of successfully assigned tasks for the algorithm PS is less than 42%, so it can be concluded that DUNM is more effective in the process of satellite schedule. In contrast, the rest of the algorithms are less effective. The rest of the algorithms are less effective. This result is because the dynamic scheduling algorithm can be adjusted in real-time according to the task requirements during the execution of the satellite scheduling task to improve resource utilization and scheduling efficiency and can cope better when the dataset is larger. As shown in Figure 10, according to the line graph, we can more intuitively find that our proposed algorithm increases the number of successfully assigned tasks as the dataset continues to grow, and the number of unsuccessfully assigned tasks has been 0. At the same time, the revenue per task is also low. As in Table 4, in the task revenue comparison results data table, the table describes the data on the number of tasks that have been assigned, the number of unsuccessfully assigned tasks, and the task revenue by the six algorithms for different task cycles.DUNM, PS [45], DS [44], NS [46], Barrier [47], and Sifting [48] represent the six different satellite scheduling algorithms. Each task has data corresponding to the assigned tasks in each mission, and each algorithm has data such as the assigned tasks, the unassigned tasks, and the task revenue. For example, in the T1 task, algorithm SUNM has eight assigned tasks and 0 unassigned tasks, and the revenue of these eight tasks is 4.42; similarly, for the other tasks, the assignment of algorithm SUNM and the corresponding revenue can be observed. The same is true for the other algorithms, and this table shows their allocations in the tasks and the corresponding revenue, respectively. Figure 10: Performance comparison of various algorithms on different task datasets. (**a**) Successful task assignments. (**b**) Unsuccessful task assignments. (**c**) Total revenue comparison. At task T1, our proposed DUNM algorithm has successfully allocated all the tasks, 8, and the rest of the algorithms have failed to allocate half of the tasks, 4. It is more obvious when there are many tasks. For example, at task T4, our proposed algorithm has successfully allocated 31 tasks, and the rest of the algorithms have allocated six tasks, which is less efficient in comparison. Regarding the task revenue for completing the scheduling tasks, our proposed DUNM Sofa, although it appears larger in the table, is because our algorithm completes more scheduling tasks. For example, in scheduling task T4, algorithm DUNM completes 31 tasks with a task gain of 56.32. PS algorithm [45] completes six tasks with a task gain of 14.5. Other algorithms are similar. In comparison, our algorithm completes scheduling tasks with higher gains, sufficient to show the high gain of our proposed algorithm advantage.