## 5 Dynamic Network Resource Allocation Algorithm Based on Accelerated ADMM  
### LEO Satellite Network Resource Allocation Modeling  
After compensation based on Equation (17), the remaining resources of node \(S_{i}\) at time \(t\) are \(\stackrel{{\leftarrow}}{{\mathrm{R}}}_{S_{i}}(t)=\left[ \stackrel{{\leftarrow}}{{\mathrm{B}}}_{S_{i}}(t),\stackrel{{ \leftarrow}}{{\mathrm{C}}}_{S_{i}}(t),\stackrel{{ \leftarrow}}{{\mathrm{S}}}_{S_{i}}(t)\right]\). In the network, there are periodic services denoted as \(\mathrm{F}_{per}\), high-bandwidth services denoted as \(\mathrm{F}_{band}\), and computing-storage services denoted as \(\mathrm{F}_{c-s}\), with their respective priority weights being \(\{w_{Z},\;\omega_{B},\;\omega_{C}\}\). The total resource demands are set as \(\{R_{Z},R_{B},R_{C}\}\) for each service, with the corresponding coefficient matrices for bandwidth, computing, and storage resource consumption for each type of service being:  
\[\begin{bmatrix}\alpha_{z}&\beta_{z}&\gamma_{z}\\ \alpha_{b}&\beta_{b}&\gamma_{b}\\ \alpha_{c}&\beta_{c}&\gamma_{c}\end{bmatrix}, \tag{19}\]where \(\alpha_{i}\) is the bandwidth resource consumption coefficient, \(\beta_{i}\) is the calculation resource consumption coefficient, and \(\gamma_{i}\) is the storage resource consumption coefficient.  
The issue of network resource allocation is addressed by maximizing the service resource coverage ratio (SRCR) in order to maximize fulfillment of resource requirements among various services and enhance the utilization rate of network resources across the entire network. This ensures the highest number of service requests in the network, thereby guaranteeing resource utilization and enhancing network service performance. Considering the limited resources on the satellite and the complex spatial and temporal characteristics of the distribution, maximizing the SRCR can improve the robustness of resource utilization, avoid the reduction in service capacity in other regions due to excessive resource reservation, and ensure the critical coverage of high-priority services. The quantity of resources assigned to each of the three types of services by node \(S_{i}\) is \(\left\{\chi_{S_{i}}^{z},\chi_{S_{i}}^{b},\chi_{S_{i}}^{c}\right\}\). The optimization objective is to maximize the SRCR, and the optimization objective function is as follows:  
\[\max\varepsilon_{\text{SRCR}}=\sum\limits_{i=1}^{\text{N}}\left(\frac{\omega_ {Z}\cdot\chi_{S_{i}}^{z}}{\text{R}_{Z}}+\frac{\omega_{B}\cdot\chi_{S_{i}}^{b}} {\text{R}_{B}}+\frac{\omega_{C}\cdot\chi_{S_{i}}^{c}}{\text{R}_{C}}\right) \tag{20}\]  
s.t. \[\text{C1}:\alpha_{z}\cdot\chi_{S_{i}}^{z}+\alpha_{b}\cdot\chi_{S_{i} }^{b} \leq \tilde{B}_{S_{i}}(t),\,\forall i\in\text{N}\] \[\text{C2}:\beta_{z}\cdot\chi_{S_{i}}^{z}+\beta_{c}\cdot\chi_{S_{i }}^{c} \leq \tilde{C}_{S_{i}}(t),\,\forall i\in\text{N}\] \[\text{C3}:\gamma_{b}\cdot\chi_{S_{i}}^{b}+\gamma_{c}\cdot\chi_{S_ {i}}^{c} \leq \tilde{S}_{S_{i}}(t),\,\forall i\in\text{N}\] \[\text{C4}:\sum\limits_{i=1}^{\text{N}}\chi_{S_{i}}^{z} \leq \text{R}_{Z}\] \[\text{C5}:\sum\limits_{i=1}^{\text{N}}\chi_{S_{i}}^{b} \leq \text{R}_{B}\] \[\text{C6}:\sum\limits_{i=1}^{\text{N}}\chi_{S_{i}}^{c} \leq \text{R}_{C},\]  
where C1 to C3 represent node resource constraints, signifying the individual resource capacity limits of each node. Specifically, the aggregate resources allocated by each node to various services must not surpass its current resource surplus, with bandwidth, computing, and storage resources all subject to these constraints. C4 to C6, on the other hand, represent the resource demand constraints of the entire network, establishing upper limits for the allocation of three types of services across the network to prevent resource wastage or over-allocation.