# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
###### Abstract
It detects congestion by measuring the dwell time of each Data packet in the router and then marks certain Data packets as congestion signals. Upon receiving these marked Data packets, consumers reduce the sending rate of Interests. The NDN Congestion Control Based on Queue Size Feedback (NDN-QSF) [19] requires routers to estimate upstream bandwidth to regulate the forwarding of Interests and uses queue size as congestion feedback to inform downstream routers to slow down Interest forwarding. The Cooperative Hybrid Congestion Control (CoopCon) scheme [20] employs four types of congestion markers to adjust the sending rate of Interests. However, these methods rely on numerous threshold parameters, and the optimal thresholds vary with applications, making them unsuitable for LEO satellite networks that carry diverse services. In summary, current methods control the traffic of Data packets by limiting the transmission rate of Interests in NDN. However, applying these methods to a satellite-based cloud platform may encounter the following issues:  
Figure 1: Data forwarding process in NDN. Among the existing methods, network throughput is adjusted to match the bandwidth of the bottleneck link. By preventing full occupation of the bottleneck link, congestion is avoided. However, this results in low throughput and high flow completion times. * The size of each Data is defined by the producer; thus, the sizes of Data packets retrieved by different Interests can vary significantly. Therefore, adjusting the sending rate of Interests alone cannot proportionally regulate the traffic of Data packets. * The satellite-based cloud platform, characterized by its time-varying nature and significant propagation delays, may experience delayed congestion responses when existing congestion feedback mechanisms are used. This can lead to packet loss [21]. * Existing schemes do not consider prioritizing packet scheduling while performing congestion control, which poses challenges in meeting the low-delay requirements for computing services and the high-throughput demands for storage services [22; 23]. In order to achieve high throughput, low transmission delay, and lossless forwarding within a satellite-based cloud platform, we propose a Congestion Control method with Lossless Forwarding (CCLF). Unlike previous approaches that adjust the transmission rate of Interests, we utilize the inherent CS of NDN to buffer Data packets and then schedule them. Each router regulates locally forwarded Data packets to avoid congestion. Currently, existing cloud server instances provide up to 5888 GB (5.8 TB) of Dynamic Random Access Memory (DRAM) [24], supporting efficient data buffering and fast access in CS. Additionally, we design a priority-based scheduling mechanism to facilitate hierarchical, efficient, and lossless forwarding at the network layer while maintaining high throughput. The main contributions of this paper are summarized as follows:  
* We propose a lossless congestion control method termed CCLF, which enhances the content storage and data forwarding mechanisms of NDN. By monitoring output queues and regulating data forwarding, CCLF effectively eliminates packet loss. * We optimize the aggregation and unified scheduling of Data packets, fully utilizing the bandwidth resources in the time-varying network. This approach overcomes the throughput limitations imposed by the constrained bandwidth of the end-to-end bottleneck link, thereby achieving high network throughput. * We design a priority-based scheduling algorithm that supports swift forwarding of high-priority Data while ensuring appropriate bandwidth allocation for ordinary Data with the same priority, thus meeting the desired Quality of Service (QoS). * We conduct a series of experiments to evaluate the CCLF method, comparing its performance with that of the recognized PCON approach. The results show that CCLF significantly reduces flow completion time and improves network throughput. The rest of this paper is organized as follows. In Section 2, we discuss the related works.