# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Multi-Layer Satellite Networks
#### 4.2.3 Traffic Balancing Dynamic Routings
However, Maximum traffic can only go through GEO satellites regardless of network congestion, increasing end-to-end delay. Based on the idea of traffic splitting, the work in [97] dispersed the LEO satellite traffic that occurred congestion to the MEO satellites. By using the idea of an adaptive routing protocol for QoS (ARPQ), calculating the maximum traffic threshold of the link was proposed in [98]. When the traffic transmitted on the link exceeds the threshold, the congested traffic is assigned to the MEO satellite. Otherwise it is transmitted directly to the LEO satellite. ARPQ is a two-layer satellite routing protocol with some congestion control mechanism, but it will cause congestion on satellite links at the LEO and MEO layers. To address this concern, the work in [99] introduced SLSR. SLSR takes into account the latency of the network and the expected waiting delay when calculating routes. Such an approach allows network data traffic to be evenly forwarded over multiple lightly loaded links instead of the shortest link. To better utilize ISLs and achieve load balancing across the constellation, in 2015, the work in [100] proposed a traffic-load-aware routing (TLAR). The scheme transforms the traffic congestion question into a convex optimization problem. The approach reduces the end-to-end latency of the network by minimizing network traffic while maintaining high throughput. However, since the time-varying network topology is not introduced into the network, the reliability of routing cannot be fully guaranteed when large-scale data transmission is performed in the LEO satellite network. Due to the complex network structure, diverse transmission traffic, and dynamic multi-layer satellite networks, the traditional traffic scheduling algorithm is difficult to model and analyze. In 2017, the work in [101] introduced a new traffic balancing algorithm for LEO-MEO networks. The algorithm exploits multi-path routing algorithms to improve link utilization. By accessing the LEO-MEO network and transmitting data in conjunction with real-time status information about the current link, the transmission queue in the link avoids network congestion. In multi-layer satellite networks, owing to the non-equal distribution of users, LEO satellites are prone to massive network congestion when they pass through areas of high traffic. Moreover, LEO satellites in the same region may share a GEO manager to optimize the topology and congestion control, causing link congestion. In 2018, the work in [102] suggested a new two-layer satellite load-balanced cooperative data transmission scheme. Each MEO satellite manages LEO satellites in groups, calculates routing tables, and collects routing information in the routing scheme. During data transmission, when congestion occurs in the LEO layer, each LEO satellite adaptively selects MEO satellites for data forwarding based on the MEO layer link status report. To a certain extent, the scheme can effectively alleviate the congestion of MEO satellites. The method optimizes the LEO-MEO satellite network topology, addressing MEO traffic congestion. After analyzing the dynamic characteristics of each layer of the satellite, the authors presented a fast topology-based conversion algorithm. The algorithm decreases the topology snapshot switching frequency and improves communication efficiency in satellite networks. In addition, to implement satellite services that satisfy QoS, the author simultaneously proposed a service-blocking-based network management mechanism. However, the traffic balancing mechanism fails to account for the queuing delay in the link. Similarly, the existing dynamic balanced routing for traffic focuses on end-to-end delay. Nevertheless, such routing algorithms perform better only when the traffic is not overloaded. As the complexity of the network increases, multi-layer satellite network service requirements grow, causing a high packet loss rate and long queuing delay.