# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 4 Resource Request Queue Reconstruction and Collaborative Scheduling Method
### Problem Conversion
\(U_{s}^{r}(t)f_{s}^{r}(t)B_{s}^{r}(t)L_{s,s^{\prime}}(t)\) denotes the time consumption of a mission \(r\) in satellite \(s\) on some time gap \(t\) transmit to the target satellite or ground base station in the presence of a visible time window. If there is no visible time window, the time consumption is zero. \(B_{s}^{r}(t)L_{s,s^{\prime}}(t)E(r)\) denotes the execution time consumed by the task from the beginning to the end of execution if there is a visible time window for the satellite and the number of resources required by the task can be satisfied. \(C1\) indicates that if the resource request operation is executable, the data transmission related to resource scheduling should be completed within the specified time. \(C2\) indicates that the related data transmission of the resource request operation should be executed within the visible time window. \(C3\) indicates that the amount of data transmitted after a successful inter-satellite link is established must be greater than the unit data and the bandwidth occupied by the currently transmitted data must not be greater than the link bandwidth threshold. \(C4\) indicates that the time consumed for unit data transmission must be more significant than 0 and less than the specified threshold \(\zeta\). \(C5\) represents the value range of the resource scheduling state of the inter-satellite link. \(C6\) indicates that multiple resource request operations use the same satellite resources, and there should be enough conversion time. \(C7\) manifests that the satellite antennas number should be a positive number. \(C8\) means that the number of links that any satellite antenna can use should be positive. \(C9\) represents the flow conservation constraint. \(C10\) represents the link connectivity state matrix. In fact, the constraints of the above conditions are far from sufficient in solving the actual problem, such as the influence of power constraints and space radiation on satellites. However, it is necessary to ignore the constraints that have minimal impact on the problem to illustrate the main problem of resource scheduling more clearly. Therefore, we consider the main constraints and propose a simplified model to minimize the resource scheduling time consumption. The model proposed in this paper is also applicable to more complex application scenarios by adding constraints related to specific application scenarios. To solve the minimum time consumption problem of resource scheduling, we investigate the method of collaborative idle satellites to share the pressure of global resource requests and achieve the goal of optimal resource scheduling in the global context. Suppose a satellite network \(G(V,E)\) is given, where the edge \((U,V)\in E\) represents the inter-satellite connection link and \(V\) represents the satellite node. Suppose there are \(K\) requesting jobs \(K= \begin{bmatrix}k_{1}&k_{2}&\cdots&k_{n}\end{bmatrix} \) with different resources. \(s_{\mathit{source}}\) indicates the source satellites in the network \(G\). We need to reconstruct the task queue and send tasks to \(s_{target}\). The target satellites \(s_{target}\) are responsible for completing the resource scheduling requirements of the allocated resource request operations. \(K_{i}=\left(s^{i}_{source},s^{i}_{target},d_{i}\right)\) denotes the resource demand \(d_{i}\) of the \(i\)-th resource request operation from satellite \(s^{i}_{source}\) to satellite \(s^{i}_{target}\). The flow cause by the task \(r\) along the edge \((u,v)\) at a slot \(t\) is \(f^{r}_{u,v}(t)\). From the above definition, the global reallocation of resource request queues can be transformed into an inter-satellite link resource contention problem and a multi-satellite resource coordinated allocation optimization problem. Furthermore, the optimization of inter-satellite link resource contention can maximize data transmission in resource request queue redistribution. The solution of the multi-satellite resource coordination optimization problem can minimize time consumption in the resource scheduling process.