# A Dynamic Service Reconfiguration Method for Satellite-Terrestrial Integrated Networks
## 5 Simulation Experiment and Analysis
### Result Analysis  
Considering the four performance indicators of algorithm convergence, end-to-end delay, service reconfiguration success rate, and algorithm running time, the proposed service migration reconfiguration method based on the fuzzy quantum genetic algorithm (FQGA-SR) is compared with the SEC-SM algorithm [14], TS-MAPSCH algorithm [16], and MGA algorithm [13] to prove the effectiveness and performance of the proposed algorithm. From the comparison of algorithm convergence in Figure 3, it can be seen that the proposed FQGA-SR algorithm converges to a stable state at the fastest speed and tends to be stable when the number of iterations reaches about 58. The speed of the TS-MAPSCH and SEC-SM algorithms is second, both tend to be stable when the number of iterations is about 200. The convergence speed of the MGA algorithm is slow and converges at about 280. At the same time, the proposed FQGA-SR algorithm and TS-MAPSCH algorithm perform well in the optimization results of end-to-end total delay. The FQGA-SR algorithm introduces quantum chromosome coding and quantum crossover processes, which greatly improves the computational efficiency, so the solution effect is the best. The MGA algorithm adopts the traditional chromosome coding method, which is difficult to solve in the dynamic network demand and large-scale problem space. In order to avoid converging to the local optimum, it needs to sacrifice the solution speed to improve the solution accuracy, so the convergence speed is slow. Figure 4 shows the change of end-to-end delay with the number of service requests under different traffic load states, in which Figure 4a,b show the states of 60% and 90% traffic load, respectively. It can be seen that the optimization results obtained by the proposed FQGA-SR algorithm are superior to the MAG algorithm, TS-MAPSCH algorithm, and SEC-SM algorithm. Among them, the convergence speed of the MGA algorithm based on the genetic algorithm is slightly slower, so the fluctuation range is slightly larger than that of other algorithms. The node selection of the SEC-SM algorithm mainly depends on the shortest path algorithm and does not fully consider the resource state, so the end-to-end delay performance of deployment results is less efficient. Overall, with the increase in the number of service requests, the remaining resources in the network continue to decrease. The deployment and migration of subsequent service requests are affected to a certain \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Computing** & **Bandwidth** & **Processing Delay/ms** \\  & **Resources/Units** & **Resources/Mbps** & **Processing Delay/ms** \\ \hline Core cloud node & 2000 & [800, 1000] & [0.1, 0.3] \\ MEC node & [200, 400] & [400, 500] & [1, 3] \\ Leo node & 200 & [200, 300] & [2, 5] \\ SFC request & [1, 5] & [1, 5] & / \\ VNF & [1, 5] & [1, 5] & / \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters of service migration. extent, and the value of end-to-end delay increases slightly. Comparing with Figure 4a,b, when the traffic load reaches 90%, the remaining available resources in the network are less, and the choice of nodes and links that can be migrated and deployed is less, so the end-to-end delay increases relatively as a whole. Figure 5a,b shows the service reconfiguration request arrival rates, respectively \(\lambda=0.05\) and \(\lambda=0.2\), and the service reconfiguration success rate changes with the number of reconfiguration requests. Taking Figure 5a as an example, it can be seen that the overall performance of the FQGA-SR algorithm is the best. When the reconfiguration request reaches 250 it can still maintain the service reconfiguration success rate of 0.847. The second is the MGA algorithm, the third is the TS-MAPSCH algorithm, and the last one is the SEC-SM algorithm, with a success rate of only 0.765. The reason is that the proposed FQGA-SR algorithm fully considers the node mobility and the current network topology state, so it can better make the migration and reconfiguration decision.