With the continuous expansion of low-Earth-orbit (LEO) satellite networks, the services within these networks have exhibited diverse and differentiated demand characteristics. Due to the limited onboard resources, efficient network resource allocation is required to ensure high-quality network performance. However, the dynamic topology and differentiated resource requirements for diversified services pose great challenges when existing resource awareness or prediction methods are applied to satellite networks, resulting in poor awareness latency and the inaccurate prediction of resource status. To solve these problems, a network resource allocation method based on awareness–prediction joint compensation is proposed. The method utilizes the node awareness latency as a prediction step and employs a long short-term memory model for resource status prediction. A dynamic compensation model is also proposed to compensate for the prediction results, which is achieved by adjusting compensation weights according to the awareness latencies and prediction accuracies. Furthermore, an efficient, accelerated alternating-direction method of multipliers (ADMM) resource allocation algorithm is proposed with the aim of maximizing the satisfaction of service resources requirements. The simulation results indicate that the relative error between the compensation data and onboard resource status does not exceed 5%, and the resource allocation method can improve the service resource coverage by 15.8%, thus improving the evaluation and allocation capabilities of network resources.

Satellite network awareness is a key technology for obtaining the status of onboard resources and supporting efficient network management. However, the dynamic characteristics of the network topology results in dynamic transmission delays of awareness data, while the instability of satellite-ground links poses challenges in selecting awareness reporting nodes. Therefore, a dynamic reporting nodes selection method for network awareness based on active-passive integrated network telemetry (DRNSM-APINT) is proposed. Specifically, an APINT model is built which can dynamically select the optimal reporting node and reduce awareness time through the bidirectional aggregation of telemetry packets along the service path. To address the requirements of various services, flexible division strategy through virtual boundaries and the integration of APINT models are combined to achieve customized selection of multiple reporting nodes on each path. Additionally, a lightweight Q-learning algorithm is proposed to dynamically solve the above issue with lower computational complexity. The simulation results demonstrate that the DRNSM-APINT method can decrease the awareness time by 34.5% and improve the overall awareness performance by 17.1%, thereby significantly enhancing the timeliness performance and dynamic characteristics of network awareness.