## 5 Simulation and Performance Analysis
### Migration Algorithm Performance Evaluation  
The network was initially configured with 100 nodes and 500 randomly generated links, which was around the size of a medium-sized ISP network [23]. Each node was given 10 normalized units of storage, computing, and networking resources, plus a random value between 1 and 10 units for inter-link delay. The service traffic in the network was random, so the corresponding virtual network functions' occupancy of resources was dynamically changing. It was assumed that each network service function chain consisted of two to five virtual network functions, one to five virtual network functions placed on each node, and a certain number of idle nodes, i.e., nodes where no virtual network functions were placed, added to the network to improve the stability of the system. The migration of a node in the network was determined by a migration trigger threshold and triggered when any resource utilization rate of the node that the virtual network function was on reached the threshold. Contrary to conventional periodic migration, the adaptive nature of the approach used in the present study was reflected in this trigger threshold-determined migration. The migration cost was defined as a function of the migration overhead and the number of migrations, where the migration overhead was the sum of the time spent on VNF migration calculation and the time spent on migration implementation, and the number of migrations was the number of VNF migrations that occurred during system operation; in the simulation experiments, it was assumed that the demand in the network was updated every second, and the VNF resource occupation on the corresponding node changed accordingly, and after 24 h of continuous updates, the migration overhead per unit time and the number of migrations per unit time were calculated to compare the performance of each algorithm. Table 3 shows a detailed comparison of the similarities, differences and expected benefits of the four experimental methods. The first method used the RAIL&TOPSIS cooperative resource-aware algorithm. To find the best migration solution using a combination of the two methods, the VNFs to be migrated were chosen to use the RAIL algorithm for resource awareness, and the overloaded nodes most in need of VNF migration were chosen to be added to the VNF migration sequence through the migration index, and the ideal destination nodes were chosen to use the TOPSIS multi-objective decision-making algorithm, but this algorithm would increase the computational complexity and migration computation time. The second algorithm was the fixed weight method; this algorithm uses fixed weights in calculating the VNF to be migrated. The third algorithm was the simple quick migration algorithm, which simplifies the computational complexity and accelerates the migration speed in the migration destination node selection stage, and uses the node as the migration destination node when the performance and delay constraints are satisfied. The fourth algorithm combined the pre-calculation RAIL and TOPSIS algorithms to reduce the solution time and migration overhead by adding pre-calculation in collaboration with real-time computation. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Compared Items**} & **An Existing High-Orbit** & **Digital Satellite-Borne Base** \\  & **Satellite Base Station System** & **Station System** \\ \hline Single functional unit & \(\geq\)30 min & \(\leq\)60 s \\ deployment time & \(\geq\)4 h & \(\leq\)120 s \\ \hline System software upgrade time & \(\geq\)4 h & \(\leq\)60 s \\ \hline Single functional unit & \(\geq\)4 h & \(\leq\)60 s \\ reconfiguration time & Not functional & \(\leq\)60 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deployment Performance Comparison. Figure 5 shows the comparison of the total migration cost of the system under different migration algorithms.