# Satellite Network Task Deployment Method Based on SDN and ICN
## 5 The Simulation Result
### Analysis of Task Deployment Method Based on Genetic Algorithm  
Here, we simulated the task deployment method of a satellite network based on the SDN. The simulation parameters include task size, link state, node state, and so on. To describe the computing speed of the processor, the concept of MIPS (million instructions per second) was introduced into the simulation, which means the number of million instructions per second executed by the processor. The simulation parameters are set as shown in Table 1.  
Figure 5: The 3D view of satellite constellations. Figure 6: The 2D view of intersatellite link. The processing speed of the LEO satellite's processor was set to MIPS, and the processing speed of the ground computing center was set to MIPS. The available capacity of the onboard cache space was randomly distributed between 20 MB and 100 MB. The computation amount of the task was set to be between MI, and the input data amount of the task was set to be between 20 and 50 MB. Each task was decomposed into up to five dependent subtasks. The maximum bandwidth of the intersatellite link was set to 100 Mbps. The above-ground bandwidth and downlink bandwidth were set as 200 Mbps. In addition to providing edge services, satellite networks also need to maintain normal communication business. Therefore, the actual availability of resources was evenly distributed between 0% and 100%. We first examined the convergence of the genetic algorithm in satellite network task deployment. In the simulation experiment, the population size of the genetic algorithm was set as 100, the number of iterations was set as 200, the crossover probability was 0.7, the mutation probability was 0.01, and the generation gap was set as 0.95. For initiated task nodes, the number of nodes used by the algorithm for deployment was set as nine. The task breakdown number was five--that is, the task was deployed on five nodes where cache space and computing resources were available. The mission input data can be obtained from multiple satellites on the same orbital plane as the mission-origination node. Figure 7 shows the algorithm convergence when the genetic algorithm is used to deal with satellite network task deployment under the state of a random network. As the number of iterations continues to increase, the value of the target utility function continues to decrease--that is, the individual's fitness degree gradually increases and converges after 100 generations of iteration. At the same time, the change data of the utility function are fit to better represent the performance of the algorithm. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Satellite CPU processing speed & \(1\times 10^{3}\) MIPS \\ Ground computing center processing speed & \(1\times 10^{8}\) MIPS \\ Task calculation & [\(2\times 10^{4}\), \(6\times 10^{4}\)] MI \\ On-board cache space & [20, 100] MB \\ Number of task decomposition & 5 \\ Intersatellite link bandwidth & 100 Mbps \\ Satellite–ground uplink and downlink bandwidth & 200 Mbps \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameter. Figure 7: The utility function changes during iteration. The computational complexity increases the system response delay. Resource-intensive tasks also bring a large link burden due to the large amount of data required by the tasks. For users, initiated delay-sensitive tasks require the network to do its best to deliver the response. For resource-intensive tasks with time-delay insensitivity, the optimization objective is to reduce the network link burden. We used the method of adding balance parameters to the objective function to balance the relationship between the two. Figure 8 shows the changing relationship between the utility function and the equilibrium parameters. As can be seen, with the increasing of the parameters, the value of the utility function also keeps increasing. The increment parameter indicates the proportion of increment delay in task processing.