# Satellite Network Task Deployment Method Based on SDN and ICN
## 3 System Model and Problem Formulation
### Scene Overview
In step four, the SDN controller sends the subtasks to the task deployment satellite nodes through the control link. The computing data required by the task are decided by the SDN. The path between the content source node and the destination is then constructed by distributing the flow table to complete the data search process. Step five means that, after the task is calculated at each task deployment point, due to the dynamic nature of the satellite and the task initiator, there may be a great time delay from task start to task completion. Therefore, it may cause the task initiator to change its position and connect to different LEO satellite edge nodes when the task is completed. Therefore, here, it is necessary to determine the coverage satellite node of the task initiator through the SDN controller on the GEO with a wider coverage, and then send it back to the satellite by each task deployment point. Then, the satellite node integrates the subtask results. Step six is to return the task result to the task initiator. A graph structure used \(G(V,E)\) to represent the network topology of the LEO satellite network when the task is initiated. In this setting, \(V(G)\) represents the set of LEO satellite nodes within the allowable range of network delay, and \(E(G)\) denotes the intersatellite link of the satellite nodes. Furthermore, \(M=\{m_{1},m_{2}\ldots m_{n}\}\) represents the set of all satellite numbers of the whole network and \(L_{m}(e_{m},v_{m})\) denotes the resource status that the LEO satellite node can provide. We also define \(e_{m}\) as the computing resources that  
Figure 2: The SDN-based task distribution diagram. the LEO satellite node can provide, and \(v_{m}\) as the cache space that the LEO satellite node can provide. In addition to the edge computing nodes and cache nodes on the satellite, the ground cloud computing center should also be considered in this paper. The ground cloud computing center is centralized processing and the edge computing can be edge processing, complementing each other. Because the computing power of the edge computing node is very limited, if the satellite edge computing resources are insufficient, the task can be sent to the remote cloud for processing. The remote cloud computing center has rich storage and computing resources. The cloud computing center and satellite edge computing nodes cooperate with each other to achieve center-edge collaboration, networkwide computing power scheduling, networkwide unified management and control, and truly provide \"ubiquitous\" services. Therefore, this paper sets that the computing resources and cache resources provided by it exceed the sum of the resources of the whole LEO satellite network. In this paper, computing tasks are divided into computing intensive and resource intensive according to requirements. The analysis of monitoring data for tasks that consume large amounts of computing resources, such as unprocessable computing tasks, is classified as computing intensive. The tasks of video compression processing and big data analysis in the IoT scene are classified as resource intensive. Computing intensive applications consume a lot of computing resources of satellite edge computing nodes, while resource intensive applications occupy a lot of on-board storage resources and intersatellite link bandwidth brought by data transfer. Therefore, the optimization goal of this paper is to minimize the task processing delay and the traffic between intersatellite links.