## 4 Performance Evaluation and Analysis
### Comparison of Algorithm Performance Under Different Loads  
Figure 4a displays the average service latency caused by each algorithm under different numbers of concurrent services. The results indicate that the SP strategy generally exhibits the lowest average latency, as it consistently selects the path with the minimal propagation delay. However, as the number of concurrent services increases, the queuing delay caused by the SP strategy gradually rises, progressively diminishing its latency performance advantage relative to other algorithms. The GreenSR algorithm consistently shows higher latency in all tests, primarily because it does not fully consider network performance in topology planning, preferentially routing traffic to satellites with higher remaining  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Orbital Altitude & 550 km, 570 km \\ Number of Orbits & 36, 18 \\ Satellites per Orbit & 22, 20 \\ Orbital Inclination & \(53^{\circ}\), \(70^{\circ}\) \\ RAAN & \(180^{\circ}\) \\ Inter-Satellite Laser Link Bandwidth & 10 Gbps \\ Ground-to-Satellite Link Bandwidth & 8 Gbps \\ \(P_{OS}\) & 100 W \\ \(P_{other}\) & 150 W \\ \(\eta/\xi/\rho_{S}\) & 1/1/1.5 W/Gbps \\ \(\varphi\) & 0.1 \\ \(\lambda\) & 1.4 \\ \(A\) & 0.6 \\ \(iter_{\text{max}}\) & 40 \\ Service Bandwidth & 50 MB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation settings.  
battery, often resulting in detours that increase both propagation and queuing delays. The SGA achieves energy savings by optimizing network topology but does not consistently outperform SP and GreenRP across all service concurrency scenarios, revealing its latency performance shortcomings under high loads.  
In contrast, the GreenRP algorithm proposed in this paper not only considers the construction of energy-saving topologies during the iterative process but also the impact of topology on latency performance, putting only those payloads minimally affecting current service transmission to sleep, thereby outperforming GreenSR and SGA in latency efficiency. With the increase in the number of concurrent services, the incremental delay for all strategies gradually decreases, indicating that as the network reaches its capacity limit, the increase in the packet loss rate of additional services causes the variation in average latency to stabilize. Overall, while ensuring network performance, GreenRP performs better in reducing latency compared to GreenSR and SGA, with an average reduction of about 3%.  
Figure 4b shows the packet loss rate performance of different algorithms. SP generally offers the lowest packet loss due to its preference for the shortest pathways, minimizing delays. GreenSR, while initially effective at lower service levels due to its traffic distribution strategy, suffers at higher loads due to its prioritization of battery life over traffic demands, leading to increased losses. SGA optimizes topology but does not surpass SP or GreenRP. SGA usually requires a relatively large number of iterations to converge, especially when dealing with complex combinatorial optimization problems. Moreover, it may perform poorly under resource constraints, resulting in the failure to achieve optimal performance such as latency. In contrast, GreenRP dynamically adjusts dormant terminals to ensure ample communication resources, effectively managing packet loss and significantly outperforming GreenSR by about 0.25% in high-load environments, showcasing its robustness in maintaining network performance while optimizing energy use.