# Satellite Network Task Deployment Method Based on SDN and ICN
## 2 Related Work
### Satellite Networks Converging Edge Computing  
Satellite networks are important supplements to achieve wide-area coverage. With the increasing demand for computing-intensive and Internet of Things applications, the idea of introducing edge computing into satellite networks becomes viable. Deploying edge computing services on the satellite networks extends computing services to the edge of the network. This provides nearby computing services for the satellite network access users and further improves the response speed of computing requests. With the satellite network communication mode based on the ICN, the data required for the calculation can also be obtained directly from the satellite network. In [15], possible methods of deploying edge computing on STN networks are studied and they design a task-parallel computing method to improve the QoS of the network. Limited energy consumption is also a prominent problem for satellite networks. Therefore, to provide low-latency computing services, one needs to incorporate the overall energy consumption of the satellite network. In the satellite IoT scenario, researchers try to deploy edge computing services on satellite networks. The time delay shows that edge computing and deep learning technology can increase detection delay and save backhaul bandwidth when processing IoT image data. Furthermore, by deploying edge services, multiple network resources such as computing, caching, and bandwidth in the network need to be reconfigured. Aiming at the problem of low management efficiency of multiple resources, ref. [16] proposed a dynamic resource allocation framework for fine-grained resource management.  
The introduction of edge computing services into satellite networks also brings new key technical issues. Task deployment is a technology that plays an essential role in completing computing services. There are many nodes in the satellite constellation and the resource status of the node is uneven. However, the completion of computing tasks requires comprehensive consideration of the consumption of multiple resources. The LEO satellite constellation is widely used for the coordinated deployment of multitasks due to its large number and computational load. Compared with the ground network, the task deployment in the satellite network needs to consider the topological dynamics of the satellite network and the limited resources. For instance, ref. [17] approached this issue from the perspective of overall load balancing and proposed a heuristic algorithm to find the optimal task allocation plan. In addition, ref. [18] combined the joint resource allocation problem of space and ground to design a task allocation plan and incorporated the dynamics of the system to design an algorithm based on reinforcement learning to learn the best allocation strategy.  
To sum up, the research on SDN and ICN satellite networks and edge computing task deployment has been relatively mature. However, there are few studies on the combination of edge computing technology and SDN/ICN satellite networks and there are few studies on the relevant deployment algorithms. In the future, SDN/ICN satellite networks will be called mainstream networks, so it is necessary to study the SDN and ICN satellite network task deployment methods.