# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 5 Simulation and Results Analysis
\begin{table}
\begin{tabular}{c c} \hline Orbital altitude & 550 km \\ \hline Orbital inclination & 53 \\ \hline Number of orbits & 18 \\ \hline Number of satellites per orbit & 20 \\ \hline Phase factor & 1 \\ \hline CPU per node & 64 cores \\ \hline Memory per node & 32 GB \\ \hline Storage per node & 128 GB \\ \hline \(B_{i,j}\) & 2.5 Gbps \\ \hline Number of VNFs per SFC & \(U(3,5)\) \\ \hline Input traffic \(\delta^{in}\) & \(U(50,200)\) Mbps \\ \hline \(lifecycle\) & \(U(1,10)\) \\ \hline \(Delay\) & \(U(50,100)\) ms \\ \hline \(coeff^{CPU}\) & \(U(1,3)/25\) Mbps \\ \hline \(coeff^{memory}\) & \(U(1,3)/25\) Mbps \\ \hline \(coeff^{storage}\) & \(U(1,3)/100\) Mbps \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation environment parameters. As illustrated in Figure 2 and Table 2, our proposed algorithm consistently outperforms existing solutions in operator total profits across varying request arrival rates. PPOSFC demonstrates achieving a 13.07% average increase compared to NSBSO. This improvement reflects enhanced efficiency in both readjustment cost reduction and service quality maintenance during network modifications. The algorithm also exhibits superior performance in generating cumulative revenue from service provision. The analysis of request arrival rates reveals that all algorithms experience diminishing marginal returns as arrival rates increase, with revenue optimization becoming challenging beyond 30 requests per timeslot. At high request rates, two critical issues emerge in capacity-constrained network regions: the inability to accommodate new service requests and frequent SFC readjustments triggered by delay constraint violations or resource limitations. The results confirm that escalating readjustment costs primarily drive revenue decline in these scenarios. The PPOSFC al \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Total Profits** & **Cost from SFC Readjustments** & **Service Provision Revenue** \\ \hline \multirow{8}{*}{PPOSFC} & 557.5 & 8.0 & 565.5 \\  & 715.0 & 205.8 & 920.8 \\  & 814.6 & 350.9 & 1165.5 \\  & 860.0 & 408.3 & 1268.3 \\  & 897.5 & 450.9 & 1348.4 \\  & 899.2 & 510.2 & 1409.4 \\  & 904.5 & 576.4 & 1480.9 \\ \hline \multirow{8}{*}{NSBSO} & 514.8 & 50.7 & 565.5 \\  & 647.5 & 216.0 & 863.5 \\  & 716.7 & 371.6 & 1088.3 \\  & 780.6 & 434.4 & 1215.0 \\  & 784.0 & 508.7 & 1292.7 \\  & 784.8 & 579.3 & 1364.1 \\  & 754.4 & 654.1 & 1408.5 \\ \hline \multirow{8}{*}{ABSA} & 350.7 & 214.8 & 565.5 \\  & 437.8 & 392.7 & 830.5 \\ \cline{1-1}  & 540.2 & 526.8 & 1067.0 \\ \cline{1-1}  & 584.5 & 586.7 & 1171.2 \\ \cline{1-1}  & 646.6 & 609.0 & 1255.6 \\ \cline{1-1}  & 613.5 & 704.9 & 1318.4 \\ \cline{1-1}  & 575.6 & 794.7 & 1370.3 \\ \hline \hline \end{tabular}
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Table 2: Algorithm performance on optimization objectives across different user request arrival rates. Figure 2: Algorithm performance on optimization objectives across different user request arrival rates: (**a**) total profits; (**b**) cost from SFC readjustments; (**c**) service provision revenue. gorithm's enhanced topology awareness proves particularly effective in mitigating these readjustment costs under high user service request intensity. As shown in Figure 3, we evaluated algorithm performance through SFC deployment and readjustment success rates. PPOSFC demonstrates significant improvements in both metrics, achieving 6.78% higher deployment success rates and 3.12% better readjustment success rates compared to ABSA. These results corroborate our earlier findings of reduced readjustment costs and increased cumulative profits while providing insight into the underlying performance mechanisms. All algorithms maintain satisfactory readjustment success rates under low user service request intensity. However, PPOSFC's strategic approach enables the optimal selection of low-cost migration solutions during readjustment phases.