## 6 Experiment and Analysis
### Comparative Experimental Results and Analysis  
We design two experiments to verify the advantages of using SAGIN-MEC versus terrestrial edge cloud networks and the advantages of DRL-G versus other algorithms. SAGIN's use of satellites and aircraft for cable-free communication, as opposed to a ground-based network alone, provides a significant resource advantage that significantly improves the success rate of SFC deployments. Therefore, 15 isolated hosts in the ground network are eliminated, and the remaining 32 ground hosts are used for the experiment. In total, 1000 SFCs are deployed in SAGIN-MEC and terrestrial edge cloud networks, respectively, and the comparison of their request acceptance rates is shown in Figure 7. After the chain length exceeds 8, the acceptance rate using only the ground network begins to decrease, while SAGIN-MEC is able to maintain the original acceptance level. In the case of successful deployment of SFC, the comparison of the average delay between them is shown in Figure 8. Therefore, the delay using SAGIN-MEC is always lower than that using only ground networks, with a difference of 6-15 ms. For this result, the reason is that the link that has the lowest latency can be automatically selected among terrestrial and non-terrestrial networks in SAGIN. At an altitude of 895 km, the coverage diameter of LEO is 3000 km. In this case, the delays of one-way and two-way communications to the ground are 3 ms and 6 ms, respectively, and the communication distance of the ground network is 1800 km at 6 ms. In SAGIN, DRL-G can choose the best solution for each situation. Compared with terrestrial edge cloud networks, SAGIN-MEC can significantly reduce the network service delay and improve the request acceptance rate by taking advantage of the low-latency characteristic of LEO satellites when transmitting data packets at medium distances. In SAGIN, compared with First-Fit (FF) [33]. Greedy algorithm guided by First-Fit (F-G) and Policy Gradient algorithm (PG) [34], it can prove the effectiveness of the hybrid algorithm DRL-G. Among them, FF is the classical baseline algorithm and PG is the widely adopted deep reinforcement learning algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number** & **CPU** & **BW** \\ \hline MEC & 47 & [4; 6] & [400, 500] \\ LEO-MEC & 3 & 2 & [200, 300] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Edge computing Server Resource Settings. Figure 7: The request acceptance ratio in SAGIN and terrestrial network. With 1000 SFCs deployed using different algorithms, the request acceptance rate is shown in Figure 9. In the case of short chain length, DRL-G and PG are superior to the heuristic algorithm (FF and F-G). In the case of long-chain length and resource shortage, PG is slightly worse than the heuristic algorithm, while DRL-G is significantly better than other algorithms, which can improve the acceptance rate by up to 20% compared to the F_G algorithm. The comparison results of energy consumption of different algorithms are shown in Figure 10. The average energy consumption of DRL-G is less than that of PG. However, the average energy consumption required by DRL-G is 6.6% higher than that of FF and F-G algorithms. When generating deployment policies, the DRL-G algorithm first ensures that the strict constraints on network resources imposed by VNF are met, and then works to reduce service energy consumption, while the FF and F-G algorithms optimize both resources and energy consumption. During the deployment process, DRL-G may sacrifice a certain amount of energy consumption to improve the deployment success rate. Figure 8: Average delay in SAGIN and terrestrial network. Figure 9: Comparison of average delay of different algorithms. In order to further analyze the advantages of DRL-G in this environment and the reasons for the above results, we use different algorithms to deploy 100 SFCs and make a comparative analysis of the results.