# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 4 Implementation
### Simulation Settings  
Our simulation illustrates the competitive forwarding of data flows with different priorities over congested links.  
The simulation environment is inspired by the Qianfan (G60) Constellation, a major LEO satellite project in China aimed at providing global broadband Internet services. According to available data, the inter-satellite laser communication links within this constellation can achieve transmission rates ranging from 100 Mbps to several 10s of Gbps. For ground users, the downlink bandwidth can range from 100 Mbps to 1 Gbps. Based on this channel information, we have constructed a simulation environment.  
This study focuses on LEO satellite networks that exhibit periodic changes. By leveraging the contact windows in satellite communication, the dynamic network can be modeled as a sequence of static networks. Based on this, experiments were conducted on a static network, where packet forwarding can be treated as point-to-point.  
We evaluate the performance of CCLF and typical PCON [35] in both constant-bandwidth and dynamic-bandwidth scenarios. Here, \"dynamic bandwidth\" specifically refers to the variation in available bandwidth for forwarding Data packets over a link. The parameters used in the constant-bandwidth scenario are summarized in Table 2.  
We employed a constant-bit-rate (CBR) consumer model to send Interest packets at a configurable constant rate. The timeout-based retransmission mechanism on the consumer  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameters** & **Value** \\ \hline Link type & point-to-point link \\ General link bandwidth & 100 Mbps \\ Congested link bandwidth & 10 Mbps \\ Propagation delay & 10 ms \\ Queue (maxpackets on the link) & 200 \\ Interest sending rate & 1000 pkts/s \\ Size of Content Store (CS) & 32 GB (approx. \(3.2\times 10^{7}\) packets) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
side operates effectively. Retransmitting Interest packets refreshes the input records in the PIT entry, ensuring the availability of the reverse path for Data forwarding. In particular, the periodic changes in satellite orbits render routing in LEO satellite networks predictable. Adjacent satellites exchange forwarding information stored in the PIT to ensure backhaul Data forwarding. Consequently, Data packets are not lost due to path changes. On this basis, we simulated and evaluated the CCLF method.  
First, we compared the performance of CCLF and PCON in terms of flow completion time. PCON is the most recognized NDN congestion control mechanism. It achieves congestion control through router-based congestion detection and marking, as well as user-end adjustment of Interest sending rates. PCON integrates key features of existing methods and serves as a model for receiver-driven congestion control technologies. Next, we evaluated the throughput and bandwidth utilization of CCLF and PCON. Specifically, we assessed consumer throughput and total network throughput, which reflects bandwidth utilization. Finally, we counted packet losses on congested links and verified lossless forwarding.