# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 6 Experiments and Results
### Experiments Establishment  
In order to simulate the experimental environment of satellite network, we use the ns2 simulator to simulate the experiment. All simulations about LEO satellites have been carried out on a typical LEO satellite constellation named Iridium. In this paper, the effectiveness of the algorithm in the topology of satellite network is verified. 30 stations are randomly distributed on the earth. The longitude and latitude range of stations are \([-180^{\circ},180^{\circ}]\) and \([0^{\circ},90^{\circ}]\) respectively. The detailed parameters of Iridium are shown in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Iridium** \\ \hline Altitude & 780 km \\ Planes & 6 \\ Satellites per plane & 11 \\ Inclination (deg) & 86.4 \\ Interplane separation (deg) & 31.6 \\ Seam separation (deg) & 22 \\ Intraplane phasing & Yes \\ Interplane phasing & Yes \\ ISLs per satellite & 4 \\ ISL bandwidth & 25 Mb/s \\ Link bandwidth & 1.5 Mb/s \\ Cross-seam ISLs & No \\ ISL latitude threshold (deg) & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The detailed parameters of Iridium [14].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Measures**} & \multicolumn{10}{c}{**Membership Functions for Each Term of Each Measures**} \\ \hline \(L_{A}\) & \(g^{A}(L_{A})=\) AL & \(g^{Z}(L_{A})=\) VL & \(g^{Z}(L_{A})=\) VL & \(g^{E}(L_{A})=\) ML & \(g^{E}(L_{A})=\) M & \(g^{A}(L_{A})=\) MH & \(g^{A}(L_{A})=\) H & \(g^{Z}(L_{A})=\) VH & \(g^{A}(L_{A})=\) AH \\ \hline \(L^{\text{B}}\) & \(g^{A}(L_{B})=\) AL & \(g^{Z}(L_{B})=\) VL & \(g^{E}(L_{B})=\) ML & \(g^{E}(L_{B})=\) M & \(g^{A}(L_{B})=\) MH & \(g^{A}(L_{B})=\) H & \(g^{A}(L_{B})=\) VH & \(g^{A}(L_{B})=\) AH \\ \hline \(L^{\text{C}}\) & \(g^{A}(L_{C})=\) AL & \(g^{Z}(L_{C})=\) VL & \(g^{E}(L_{C})=\) ML & \(g^{E}(L_{C})=\) M & \(g^{A}(L_{C})=\) MH & \(g^{A}(L_{C})=\) H & \(g^{A}(L_{C})=\) VH & \(g^{A}(L_{C})=\) AH \\ \hline \(L^{\text{D}}\) & \(g^{A}(L_{D})=\) AL & \(g^{Z}(L_{D})=\) VL & \(g^{E}(L_{D})=\) ML & \(g^{E}(L_{D})=\) M & \(g^{A}(L_{D})=\) MH & \(g^{A}(L_{D})=\) H & \(g^{A}(L_{D})=\) VH & \(g^{A}(L_{D})=\) AH \\ \hline \end{tabular}
\end{table}
Table 2: The membership functions.  
The buffer queue size on each node is set to 100 packets and the size of each packet is set to 1 KB. We use 1200 ON/OFF streams and the ON/OFF period of each flow follows the Pareto distribution with the shape parameter of 1.5. The average burst time and idle time are set to 500 ms. The range of individual data transmission rate is from 0.3 Mbps to 0.75 Mbps. The capacity of inter-satellite link and satellite- terrestrial link are set to 25 Mbps.  
To evaluate end-to-end delay, the flow distribution model is shown in Table 5.  
Distribution of traffic flows is determined by the density of population. Satellite traffic over densely populated cities, such as cities in North America, Europe and East Asia, is large, resulting in large queuing delay. The satellite traffic over sparsely populated areas, such as oceans and deserts, is small and the queuing time is small.  
For simplicity, we only simulate the data plane (LEO satellite constellation). The control plane is only used to calculate the routing table, which can be simulated during the implementation of the routing algorithm. ELB and DSP algorithms are two classical satellite network routing algorithms and researchers have compared their proposed algorithms with them in many papers. we compare routing algorithm based on the Interval type-2 fuzzy set proposed in this paper with the ELB routing algorithm over DSP and the centralized DSP routing algorithm in average delay, total throughput and packet drop rate