# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Multi-Layer Satellite Networks
#### 4.2.2 QoS-Based Dynamic Routings
more QoS guarantees in packet loss rate, link congestion, and other performance aspects compared to the shortest path optimization algorithm. The authors of [75] designed a novel GEO-highly elliptical orbit (HEO)-LEO satellite network architecture to improve global coverage. The QoS-aware multi-point transport routing (QAMRP) scheme was proposed based on the network architecture. In QAMRP, the multi-cast tree is built using LCT, reducing tree overhead and meeting QoS requirements. Compared with the shortest path tree (SPT) strategy algorithm, the algorithm is superior in tree delay, tree overhead, and multi-point transmission failure. Each satellite is a topological node in multi-layer satellite networks, so the whole network is a graph matrix. However, each satellite can only acquire QoS information of neighboring ISLs. Therefore, based on link congestion and QoS awareness, the work in [76] adopted a new distributed routing algorithm to estimate the global QoS conditions among satellite nodes. Simulation experiments show that the method has excellent network control and management capabilities. However, this scheme fails to address the root of the QoS routing issue. Therefore, the easiest way to meet QoS requirements in multi-layer satellite networks is to develop a routing scheme. In 2016, utilizing the ant colony optimization algorithm, the work in [77] designed a new adaptive QoS dynamic routing. The algorithm determines the optimal QoS path for the destination node by collecting the QoS status between the source and destination satellite nodes. This method effectively prevents overloading in the satellite nodes during path selection. In addition, the proposed algorithm also optimizes QoS parameters based on specific satellite scenarios, improving convergence. Compared to the traditional distributed QoS routing based on ant algorithm (DQA), the scheme has a better end-to-end delay and QoS state values. However, the algorithm ignores QoS relationships, affecting network use. The work in [78] proposed a multi-service routing algorithm for GEO-LEO dual-layer satellite networks. The scheme uses the multi-layer satellite network's dynamic topology to provide broadband service. Correlation weights can better regulate latency services by considering the remaining bandwidth and latency. Simulations show that the proposed method reduces the latency of high-priority services and improves the QoS of low-priority services. In addition, the throughput capacity of the proposed method has significant advantages in the case of high service volume. Meanwhile, it effectively solves the problem of multi-service routing and improves the communication quality of the multi-layer satellite networks. However, satellite networks are characterized by intermittent connections, extensive delays, and time-varying topologies, limiting link QoS requirements. In 2017, the work in [79] introduced a QoS algorithm for IP satellite networks. By taking link throughput and end-to-end delay as path cost factors, the algorithm solves for a demand satellite route that meets link QoS. Simulation analysis identified the most critical causes of link delay: queue delay and global link throughput. However, congested satellite links cause longer delays for high-throughput traffic. When there is a blockage in the LEO satellite network, the algorithm selects the routing path in the GEO satellite network. However, the current approach focuses largely on link latency and ignores other factors. Hence, the work in [80] designed a new QoS-enabled routing strategy. The scheme utilizes a temporal aggregation graph (STAG) to implement QoS-based routing. QoS dynamic routing is a joint optimization problem of minimizing transmission delay and maximizing throughput based on service requirements and network latency. The algorithm addresses the shortest path problem and the multiple optimal traffic paths for a given transmission delay. In addition, the algorithm maximizes traffic and minimizes latency while reducing complexity.