## 2 LEO Satellite Network Model
### LEO Satellite Network Services  
Three typical types of traffic services in LEO satellite networks were selected in order to better simulate the impact of differentiated resource demands between services on the resource variation of nodes--namely, periodic services, high-bandwidth services, and computing-storage services--with the priority of services gradually decreasing. Periodic services refer to periodic requests that are triggered on time based on the laws of topological motion or the repetitive needs of users. Typical services include daily control commands and periodic network data transmissions. Such services have relatively small data volumes and consume bandwidth resources and computing resources on the satellite. The Bernoulli binary distribution was utilized in this work to represent the success rate of a node receiving services within a fixed period [17]. Specifically, services arrive at certain intervals within a certain length of time with a certain probability. High-bandwidth services primarily target scenarios with high data rates and high-capacity transmission requirements. These services involve substantial data volumes and have high-bandwidth resource demands,  
Figure 1: LEO satellite network scenario.  
often resulting in larger transmission delays at nodes. They consume larger amounts of bandwidth resources and some storage resources on the satellite. Typical services include audio and video streaming as well as large-volume image transmission services. The average arrival rate of the service per unit time, the number of service requests, and the observation interval were used as parameters in this work to simulate the probability of service arrival based on the Poisson distribution model [18,19]. Computing storage services rely on the onboard processing and storage capabilities of satellites. Typical application scenarios include satellite protocol acceleration, routing computation, and routing caching, which often consume a significant amount of satellite computing and storage resources. A Gamma model based on the arrival intervals and time scales of service was constructed in this study in order to model the probability of service arrival [20].  
After the completion of the service, the resources occupied by the service are gradually released, and the resource consumption of the service does not exceed the maximum available resources of the satellite. Simulations were conducted for the three types of services based on the arrival probability distribution and data characteristics of each type of service. There are total of \(M\) services in the network, with the service set denoted as \(\mathrm{F_{serv}}\). Among them, the periodic service set is \(\mathrm{F_{serv}}\). Among them, the periodic service set is \(\mathrm{F_{per}}\), the high-bandwidth service set is \(\mathrm{F_{band}}\), and the computing-storage service set is \(\mathrm{F_{c-s}}\), with \(\mathrm{F_{serv}}=\mathrm{F_{per}}\cup\mathrm{F_{band}}\cup\mathrm{F_{c-s}}\) and \(\mathrm{F_{per}}\cap\mathrm{F_{band}}\cap\mathrm{F_{c-s}}=\varnothing\). The number of the three types of services are \(M_{\mathrm{per}}\), \(M_{\mathrm{band}}\), \(M_{\mathrm{c-s}}\), and \(M=M_{\mathrm{per}}+M_{\mathrm{band}}+M_{\mathrm{c-s}}\).