# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Constant Bandwidth
#### 5.1.1 Flow Completion Time  
We evaluate the completion time of flows with different priorities. The priority of Flow\({}_{j}\), which is requested by Consumer\({}_{j}\), is represented as \(p_{j}\). A larger \(p_{j}\) indicates a higher priority. The relative priorities are set as \(p_{1}=p_{2}<p_{3}<p_{4}\). Consumer 2 sends 3000 Interests, while the other consumers each send 5000 Interests. Each Interest brings back a Data packet containing 1024 bytes of content, with a total length of 1069 bytes. Both the speed of sending Interests and the speed of receiving Data are set to 1000 pkts/s.  
Figure 8: The simulated network with constant bandwidth.  
In this case, the Router 3 will handle a total of 34 Mbps of Data forwarding from four data flows, which exceeds the available bandwidth of 10 Mbps, leading to link congestion. According to the scheduling algorithm described in Section 3.4, Flow 4 and Flow 3 with high priority will be forwarded first. Then, under limited link capacity, there exists a unique optimal rate allocation for forwarding Flow 1 and Flow 2, which have the same priority. In this experiment, due to the synchronous sending of the Interest packets, the Data packets of Flow 1 and Flow 2, sent at the same time, will arrive at Router 3 simultaneously, resulting in identical queuing delays and packet reception rates. By applying these actual parameters to Equation (43), we conclude that the optimal rate allocation for Flow 1 and Flow 2 is \(x_{1}=x_{2}\). This implies that an even distribution of bandwidth among flows with equal priority, such as Flow 1 and Flow 2, leads to optimal network utility.  
By comparison, under the PCON mechanism, consumers adaptively adjust the speed of sending Interests based on congestion feedback. Additionally, the priority of Data packets is ignored during Data forwarding. We compare the performance of CCLF with that of PCON, and the results are presented in Figure 9.  
The results indicate that, using the PCON method, the FCT of Flow 3 and Flow 4 with high priority is 17.15 s and 18.09 s, respectively, both of which are close to the FCT of 18.43 s for Flow 1. The flow completion time under the PCON is only related to the content size. In contrast, using the CCLF method reduces the FCT of Flow 3 and Flow 4 to 8.7 s and 5.15 s, respectively, representing a reduction of 50% and 70% compared to PCON. Notably, the PCON method yields an average FCT of 16.60 s, whereas the CCLF method results in an average FCT of 10.79 s. This represents a 35% reduction in average flow completion time.  
This is because the CCLF method can prioritize the forwarding of Data with high priority, satisfying the transmission of content with high QoS requirements. However, PCON does not distinguish the priority or urgency of Data and instead distributes limited bandwidth evenly across all flows.  
Specifically, Figure 9 shows that the differences in completion times between consecutive priority levels are inconsistent. Our research focuses on QoS guarantees for critical real-time data, particularly satellite control information, ensuring low transmission delays for high-priority data flows. Consequently, the reduction in delay across different priority levels is not regular. Given that high-priority flows constitute a smaller proportion of network traffic, low-priority flows do not experience excessive delays or starvation. In future work, we will further quantify variations in delay to provide predictable transmission delays for data flows across different priority levels.