# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 6 Results and Discussions
\begin{table}
\begin{tabular}{c c c} \hline
**Classification** & **Configuration** & **Parameter** \\ \hline \multirow{3}{*}{Hardware} & CPU & i7 4.6 GHz \\  & Memory & 8 GB \\  & SSD & 512 GB \\  & Constellation & Walker \\  & Satellite altitude & 1200 km \\  & Number of satellites & 64 \\  & Number of satellite planes & 8 \\ Simulation & Distribution of users & Europe, China, America Poisson distribution \\  & Number of users & 1000 \\  & Protocol & RRC/SDAP/PDCP/RLC/MAC \\  & Code & LDPC/Polar \\  & Modulation & BPSK/QPSK \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters configuration. In Figure 9, we verify the effect of different algorithms on the normalized utilization of CPU, memory, and bandwidth for the different number of iterations. We can observe that as the number of iterations increases, the CPU normalized utilization of the PSO, the VNE-ARS, and the MMGA increases to a smooth maximum. However, as the number of iterations increases, the CPU normalized utilization of the SA increases and then decreases. At the same time, as the number of iterations increases, the memory normalized utilization of the SA and the PSO increases and then decreases, and the memory normalized utilization of the VNE-ARS and the MMGA increases to a smooth maximum. In addition, the bandwidth normalized utilization of the SA, the PSO, the VNE-ARS, and the MMGA increases to a smooth maximum. This is due to the overconvergence of the SA as the number of iterations increases. Since we assign the maximum weight to the optimal solution of each layer in the solution of the MMGA, it has a better chance of being inherited to the next layer, avoiding the overproduction of super-individuals and preventing premature  
convergence. Meanwhile, in the MMGA, the solutions with the lowest 10% weight values are eliminated at each iteration. In this way, as the number of iterations increases, the number of individuals to be searched for the best gradually decreases and the resources consumed do not show a linear increase. However, due to the increase in the number of computations, resources are still consumed. In addition, no iteration is performed in the NFA-LR, so the normalized utilization of CPU, memory, and bandwidth does not change as the number of iterations increases. From Figure 10, we can observe that the time consumed by different algorithms varies as the number of iterations increases. This is due to the increase in the number of iterations leading to an increase in the optimal result of each algorithm. In addition, the SA, the PSO, the VNE-ARS, and the NFA-LR are highly affected by the iteration time. The MMGA is less affected by the iteration time. This is because the cost of consumption is used as a limiting condition for finding the optimal result in the MMGA. Minimization time is considered in the search for the optimal allocation result. In this paper, we simulate the communication system using standalone architecture, parallel architecture, and serverless architecture. The performance is shown in Figure 11. As shown in Figure 11, the normalized utilization using standalone architecture is much smaller than that using the parallel and serverless architectures among CPU, memory, and bandwidth. The normalized utilization of the parallel architecture and serverless architecture is the same, because the servers of these architectures can process the tasks  
Figure 10: Normalization time consumed for different numbers of iterations. Figure 9: The CPU, memory, and bandwidth normalized utilization of different algorithms under the different number of iterations. synchronously, but the tasks of standalone architecture can only be processed in one server at the same time. In addition, the processing time of the standalone architecture is the largest. The transmission of control information increases the processing time due to a master control node needing to control all servers in the parallel architecture.