# Dynamic Routings in Satellite Networks: An Overview
## 4 Existing Works on Dynamic Satellite Routings
### Dynamic Routings in the Multi-Layer Satellite Networks
#### 4.2.1 SDN-Based Dynamic Routing
The simulation compares ASRS with the traditional snapshot routing algorithm in Open Network Operating System (ONOS). The results show that the ASRS algorithm has significant superiority in satellite network resource utilization and QoS. In multi-layer satellite networks, saving energy becomes a critical issue due to the limitations in network resources. Since the SDSN has integrated control over the entire network resources, it dramatically improves the utilization of satellite resources [66; 67]. In a sense, SDSN is a \"green\" satellite network. However, many energy losses in SDSN cannot be ignored. Therefore, reducing the non-negligible energy consumption in the network can contribute to the energy saving of SDSN. A new energy consumption model for multi-layer satellite networks was designed in [68]. For the model, an SDSN topology generation algorithm was introduced. The algorithm considers the link switching energy consumption, as well as the inter-satellite link energy consumption. Second, a DDoS control scheme was given for the vast energy consumption caused by abnormal traffic in the network. In SDSN, the abnormal traffic generated by DDoS attacks consumes many of the network's resources and severely disrupts the storage and forwarding of normal traffic in the network. As a result, the overall resource utilization of the satellite network decreases while the overall energy consumption increases. Therefore, to ensure the security of the satellite network and its efficient resource use, an intelligent traffic mitigation strategy is needed. Through performance evaluation, the new network topology generation algorithms and DDoS attack mitigation methods effectively reduce network energy consumption. In multi-layer satellite networks, single-path routing can no longer accommodate the QoS requirements of various services. In single-path routing, all packets must select the optimal path of the current link for transmission, wasting the network's limited spatial resources. In addition, due to the solid dynamic nature of multi-layer satellite networks, the switching of single routing paths also takes time, resulting in a certain degree of network delay. On the contrary, multi-path routing can provide path choices based on QoS requirements, improving network QoS. In 2021, utilizing the global topology information of SDNs, the work in [69] designed a multipath routing algorithm (MPRA) based on the ant colony algorithm. In order to minimize the average length of the links while minimizing the number of inter-layer link switches, the method employs the optimal minimum switching strategy (OMHS). The improved ant colony algorithm reduces path duplication and selects high-quality routing paths that meet QoS requirements. The algorithm uses different ant colonies to optimize multiple QoS-compliant routing paths. In multi-layer satellite networks, multipath routing provides more options for data transmission and improves network security. However, due to the limitations of the heuristic algorithm, the multi-path routing algorithm fails to guarantee that all found routes are optimal and meet the QoS requirements. Therefore, in the following research, using joint machine learning to train the algorithm can reach the expected goal and generate optimized routes. At the same time, the algorithm is stable and resistant to destruction. The work in [70] designed a LEO-GEO satellite network model based on the SDN architecture. The model includes three GEO satellite controllers and one ground controller. For two-way session services, the solution sets a high-priority queue to ensure its priority. Moreover, the approach optimized the LCRA proposed in [38]. Since there are no iterations in the computation process, the on-star storage cost is greatly reduced. QoS-aware routing (QoSRA) outperforms other routing methods in average delay, packet loss rate, and network throughput.