## 5 Simulation and Performance Analysis
### Results and Analysis  
First, we analyzed the proposed method's service access success rate performance. Figure 3 demonstrates the relationship between service access success rate and the number of functional constraints for different scales. As the number of function constraints increases, the access success rates of all algorithms decrease. The reason is that the increase in the number of functions in the SFC brings more constraints; thus, the number of paths satisfying the SFC constraints decreases. In addition, as the number of data flows increases, the access success rates of all the algorithms also decrease. The reason is that the larger the data flow scale is, the greater the probability of collision during access, and the fewer the paths that satisfy the SFC conditions. Moreover, it is observed that the algorithms proposed in this paper significantly outperform the DQN-LBR, DQR and SFC-APS algorithms, and the access success rate is improved by more than 19.1%. The reason is that the SFC-APS method involves rule-based local optimization, which makes it difficult to balance global objectives in complex tasks; the DQR method does not explicitly utilize the topological relationships of graph structures, which leads to slow training of decision networks; and the DQN-LBR approach does not consider SFC constraints, which leads to some data flows failing to satisfy their SFC constraints. Next, we analyzed the network average load performance of the proposed algorithm. Figure 4 shows the satellite node and ground station node average network load value with different numbers of data flows and different numbers of functional constraints. The performance of the algorithm proposed in this paper and DQN-LBR is better than that of the DQR and SFC-APS algorithms. The load decreased by more than 14.1%. The reason is that the DQR and SFC-APS methods did not consider the load of the network nodes and only used a path length similar to that of BFS as a search constraint, resulting in some nodes being fully loaded early. Figure 3: Access success rates of different data flows. The red curve is the result based on the reinforcement learning algorithm (DQR), the green curve is the result of the traditional graph theory algorithm (SFC-APS), the magenta curve is the result of the algorithm proposed in this paper (DQN-LBR), and the blue curve is the result of the algorithm proposed in this paper (GDRL-SFCR). FN represents the total number of functional nodes in the end-to-end path SFC constraint. Then, we analyzed the path delay performance of the proposed algorithm. Figure 5 shows the average path delay under different numbers of data flows and numbers of function constraints. As seen in Figure 5, the path delay of the GDRL-SFCR algorithm is lower than other algorithms. As the number of functional constraints increases, the path delays of all the algorithms increase. However, compared to the other algorithms, the delay of the GDRL-SFCR algorithm is reduced by more than 11.3%. The reason is that the other algorithms do not consider network loading and are not sufficiently adaptive to the dynamic environment of the mega-constellation networks. When the nodes are fully loaded, the length of the searched paths that satisfy the SFC constraints increases, and the delay increases. Figure 4: Network average load of different data flows. The red curve is the result based on the reinforcement learning algorithm (DQR), the green curve is the result of the traditional graph theory algorithm (SFC-APS), the magenta curve is the result of the algorithm proposed in this paper (DQN-LBR), and the blue curve is the result of the algorithm proposed in this paper (QN-LBR). FN represents the total number of functional nodes in the end-to-end path SFC constraint. Figure 5: Average path delay of different data flows.