## 4 Discussion
### Simulation Results and Analysis  
We assume that the data received by the satellite network follows a normal distribution [12]. The mean value of the normal distribution \(\mu\) represents the average quantity of data received from users. The variance \(\sigma\) of the distribution is set to range from 0.05\(\mu\) to 0.5\(\mu\). We compare our scheme with the method named SiRRS (Single-Resource Range Scheduling) [9], which does not utilize ISLs to transfer data between satellites.  
Changing the mean value \(\mu\) of the normal distribution allows us to simulate the performance of the scheme under two distinct data download load scenarios. By light and heavy load, the total amount of data to be downloaded is set to around 40-50% and 80-90% respectively, of the capacity of the ES. Throughput and end-to-end delay are selected as measures of scheme performance. The throughput is defined as the proportion of actual downloaded traffic to overall network traffic. End-to-end latency is defined as the time between when a packet is sent to the network and when it is received by an ES.  
Figures 9 and 10 are the simulation results of throughput under the light load and heavy load, respectively, and Figures 11 and 12 are the simulation results of end-to-end delay under the light load and heavy load, respectively. The total data load in the figures represents the ratio between the total quantity of data received by all satellites and the capacity of the ES. We can draw the following conclusions from Figures 9-12.  
Figure 8: Process model.  
Figure 9: Throughput of light data load.  
(1) From Figures 9 and 10, it is obvious that our proposed scheme (CDD) outperforms the SIRRS method in terms of throughput. The throughput performance of the CDD scheme remains close to the network load with the change of the data load. In this way, visible satellites can download almost all user data. However, in the simulation using the SIRRS scheme, the system throughput starts to decrease when the network load is heavy. The reason for this phenomenon is when the capacity of feeder links is reduced due to rain attenuation, a large amount of data still needs to be downloaded to ES through these feeder links. When the waiting time of these data in the satellite cache queue exceeds the lifetime, they will be discarded and the system throughput will decrease. The CDD scheme avoids the above situation and makes it perform better than the method that does not use ISL data transfer.  
(2) From Figures 11 and 12, our proposed scheme also outperforms the SiRRS method in terms of end-to-end delay. The end-to-end delay obtained by the simulation of this  
Figure 11: End-to-end delay of light data load.  
Figure 12: End-to-end delay of heavy data load.  
Figure 10: Throughput of heavy data load.  
scheme is still about 0.25 s when the network load is very heavy (80-90%). The average delay of SiRRS method is 0.65 s, which means that many packets will become clogged in the cache for longer than their lifetime.  
Figures 13 and 14 show the data delay distribution separately under different data loads. As can be seen from Figure 14, when the network load is very heavy, nearly half of the packet delay in the simulation results obtained by SIRRS method is more than 0.9 s, while the packet delay of CDD scheme is all less than 0.7 s. The cause of this phenomenon is that a large number of packets accumulate on feeder links with low capacity, resulting in a dramatic increase in queuing delay in SiRRS method, while the CDD scheme reduces the end-to-end delay of packets by transferring the data to satellites with abundant feeder link capacity.