# 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
In the solution, the SDN controller is employed to evaluate the blocking situation of the link and to ensure the flexibility of the hybrid satellite network simultaneously. The strategy schedules traffic to empty satellite nodes on the same layer for congested link traffic. To a certain extent, it solves the traffic congestion issue in the link. In addition, compared to traditional satellite network traffic-balancing algorithms, the method has a better ability to distribute link traffic. However, such traffic balance routing strategies lack flexibility for multi-layer satellite networks with dynamic structures. Meanwhile, such an algorithm makes it hard to perform accurate modeling and analysis. Proper end-to-end (E2E) traffic prediction in multi-layer satellite networks plays a critical role in balancing traffic. The work in [108] presented an improved Markov model (HMM)-based method for E2E traffic prediction. The GEO satellite is the main controller of the SDN system, and it can obtain some traffic data from the LEO satellite it controls. Owing to the limitation of the number of GEO satellites, the algorithm adapts the idea of gradual convergence. Simulation experiments on the HMM verify the effectiveness of the algorithm. Moreover, despite the large volatility of the actual service traffic in existing multi-layer satellite networks, the approach is still better at predicting and tracking the traffic on the links with low error. However, the proposed scheme fails to consider the dynamic characteristics of the multi-layer satellite network and uses a virtual topology instead. The routing scheme assumes the dynamic network topology is stationary in a given time interval, limiting its use in multi-layer satellite networks. To address the issue, the work in [109] designed a multi-agent reinforcement-learning-based traffic scheduling algorithm for multi-layer satellite networks. With reinforcement learning techniques, the SDN can fully combine the centralized control capability of the SDN and the dynamic adaptive capability of reinforcement learning. By using neural networks to model reinforcement learning and SDNs, the dynamics of network traffic can be accurately sensed, and routing paths can be optimized in a timely manner. The algorithm takes GEO satellites as agents for training. By improving the experience pool in reinforcement learning, it is possible to enable GEO satellites to adapt to changing link states in multi-layer satellite networks continuously. The trained traffic scheduling model can make fast routing decisions for congested traffic detected over the link and maximize link utilization. Additionally, the algorithm deploys SDN controllers on GEO satellites, allowing the monitoring of topology changes and traffic status across the satellite network. Since the research on SDN traffic detection is very mature, the algorithm balances network traffic by detecting congested traffic. Efficient traffic distribution over heterogeneous links in satellite data relay networks (SDRNs) is equally a key concern in current research. However, diverse traffic types and heterogeneous links complicate link delay evaluation, affecting traffic distribution efficiency. Therefore, the work in [110] extended spatial traffic to arbitrary categories and analyzed the effect of the link heterogeneity on link traffic distribution simultaneously. The algorithm introduces a delay upper bound in heterogeneous links to optimize traffic allocation with the least delay. Compared to the inherent characteristics of homogeneous flows, the inherent characteristics of flows within polymerization heterogeneity have been radically changed. Therefore, in future research, for the multi-layer satellite network traffic-balancing dynamic routing algorithm, how to achieve the optimal traffic distribution of heterogeneous links will become the focus of attention of academia and industry. Comparisons of the key performance of partial dynamic routings in multi-layer satellite networks are shown in Table 12.