# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 5 Simulation Experiment and Analysis  
### Parameter Setting  
In order to evaluate the effectiveness and performance of the proposed algorithm in the service migration optimization problem of STINs, a simulation platform is established. The simulation experiment runs on the host of Intel Core i7-10710u CPU @ 3.3 GHz and 16 GB Ram. The joint simulation experiment of MATLAB 2018b and STK 11 is used to simulate the satellite constellation networking and terrestrial network connection states information.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**FQGA-SR Algorithm**} \\ \hline input & Substrate physical network \(G_{sn}\), service request set \(G_{s}\), population size \(M\), chromosome length \(K\), number of evolutionary iterations \(T\) \\ output & Optimal migration strategy \(X_{b}\) \\
1 & **foreach** service request \(s\in G_{s}\)**do** \\
2 & **if**_IsRequestSatisfied_ (\(G_{sn},G_{s}\)) then \\
3 & use \(k\)-Dijkstra algorithm to calculate the shortest path and obtain the shortest path matrix \(\mathbb{S}\) \\
4 & initialize the quantum gene population according to Equation (14) to obtain \(\mathbf{N}(0)\) \\
5 & while \((t<T)\) do \\
6 & put \(H_{1}\) and \(H_{2}\) into FL framework \\
7 & population evolution iteration \(t=t+1\) \\
8 & adjusting the phase of quantum rotation gate according to FL strategy\(\Delta\theta\) \\
8 & and mutation probability \(P_{m}\) \\
9 & According to mutation probability \(P_{m}\) select chromosome coding \\
10 & **if**\(Fit(X_{c})<Fit^{*}(X_{b})\) then \\
11 & choose the qubits of \(\mathbf{N}_{i}^{(t)}\) to achieve quantum gate rotation and exchange \\ probability amplitude \\
12 & **else if** \\
13 & choose \(\mathbf{N}_{M-i}^{(t)}\) and exchange probability amplitude \\
14 & **end while** \\
15 & **end if** \\
16 & **end foreach** \\
17 & **update**_IsRequestSatisfied_ (\(G_{sn}\), \(G_{s}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: FQGA-SR Algorithm Description.  
As shown in Table 3, the satellite network operation in the STIN is simulated through STK and MATLAB joint simulation, so as to obtain the visibility of satellite nodes at all times, the sustainable time of satellite-terrestrial and inter-satellite links, and the satellite connecting to ground nodes. Assuming the SFC reconfiguration scenario is under the STIN architecture. The service migration simulation parameter settings are shown in Table 3, which is adjusted on the basis of work [24]. We set 1 core cloud node, 15 ground edge nodes (MEC nodes), and 3 LEO satellite edge nodes (Leo nodes). The satellite-terrestrial, inter-satellite, and ground link connections change with the operation of the LEO satellite nodes.  
Assume the SFCs arriving per unit of time follows a Possion distribution, which has arriving rate \(\lambda\) in range of [0.05, 0.2], each SFC contains [2,6] VNFs, the live time is [30, 80] ms, and the processing delay generated by each VNF is [120, 200] ns. The above simulation parameters obey the random distribution of their respective value ranges. The experimental results are averaged by 10 repeated experiments.