# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 4 Resource Request Queue Reconstruction and Collaborative Scheduling Method  
For multi-satellite and multi-mission high time and high load resource scheduling problems, the single-satellite resource scheduling method can no longer meet the multi-resource and high time requirements. In an onboard autonomous resource allocation environment with multiple missions and resource requests, local LEO and medium orbit satellites may face many resource requests, resulting in long resource request queues for local satellites and multiple types of resource requests that are idle resources for the remaining satellites. Therefore, the pressure of high-load resource requests is relieved based on idle satellite and link resources, effectively reducing the resource request response time and improving resource utilization. In this research direction, we propose an online dynamic scheduling and queue reconfiguration method for multi-satellite resource request queues which solves the problems of excessive resource request pressure on a single satellite and waste of idle satellite resources and achieves the purpose of autonomous redistribution and dynamic scheduling of resource request queues on satellites. This paper aims to solve the problem of collaborative scheduling of multi-satellite resources in space information networks. As shown in Figure 2, we use the block diagram [44; 45] to describe the construct of the initialized network according to the resource state, then calculate the inter-satellite link selection and satellite resource allocation strategy based on the theory of multicommodity network flow. Finally, we update the resource quantity data of the network and complete the collaborative resource scheduling among multiple satellites. As shown in Figure 3a shows that there are three satellites that have received the resource request sent by the ground station or other satellites at a slot \(t\), where \(S=\{s_{1},s_{2},s_{3}\}\) represents the set of satellites that have received resource requests in the spatial information network. The gray squares in the queue represent the number of storage resources requested, the white squares represent the number of observation resources required, and the black squares represent the number of computing resources needed. Figure 3b shows the state in which the satellite set \(S\) redistributes all received resource requests at a time \(T=t+\delta\) and cooperates with neighboring satellites to share the resource request pressure, where the time interval \(\delta\) satisfies \(\forall\delta\in N_{+}\) at this time \(\delta>0\). The satellite set \(S^{\prime}=\{s_{1},s_{2},s_{3},s_{4},s_{5},s_{6}\}\) includes the original satellite set \(S\) and other satellite sets \(\{s_{4},s_{5}\}\) that are coordinated. Each satellite in the satellite set \(S^{\prime}\) is allocated a certain amount of resource request operations according to the number and status of satellite resources. From  
Figure 1: Schematic diagram of the existing spatial information network resource scheduling process. the satellite state figure at the time \(T=t+\delta\), it can be seen that the number of resource request operations on the original satellite set \(S\) has been reduced, and the resource type has become more singular. In contrast, the idle satellite set \(\{s_{4},s_{5}\}\) has obtained a certain number of resource request operations. When faced with many resource request jobs, the single-satellite resource scheduling method is challenging in terms of meeting the high timeliness and real-time requirements. We propose a method of synergizing the resources of the current satellite and its surrounding satellites to improve the overall resource utilization of the space information network. As shown in Figure 3, the state subgraph (a) to the state subgraph (b) is the process of reducing the satellite service load. In order to clearly describe the specific process of resource request queue reconstruction and unified planning, we use spatio-temporal axes to represent the satellite spatio-temporal variation [26].