# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 1 Introduction  
The low-earth-orbit (LEO) satellite network, with its characteristics of low latency, high capacity, and seamless global coverage, has developed rapidly in recent years and has become a key technological means for achieving global wireless access [1, 2]. With the widespread adoption of time-sensitive applications such as industrial IoT and remote control, users have imposed stricter requirements on the transmission capabilities of LEO networks for time-sensitive services [3, 4]. However, the high-speed movement of satellites and frequent orbital changes lead to dynamic topology switching of inter-satellite links (ISLs) and satellite-terrestrial links (STLs), causing significant fluctuations in latency for multi-hop transmission paths [5]. Meanwhile, the limited on-board computing capabilities and the competition for uplink and downlink bandwidth further exacerbate the complexity of resource allocation [6]. Against this backdrop, how to achieve efficient schedulingof time-sensitive flows (TSFs) and ensure quality of service (QoS) has become a critical challenge that urgently needs to be addressed. Traditional terrestrial time-sensitive network technologies (e.g., cyclic queue and forwarding, CQF, based on Ethernet [7]) rely on fixed paths and static priority scheduling to provide deterministic latency guarantees for different services. However, the highly dynamic nature and resource constraints of LEO satellite networks fundamentally undermine the applicability of CQF. Firstly, frequent satellite link switching leads to the failure of preset routing, and static queue scheduling cannot adapt to dynamic path changes, resulting in excessive transmission latency [8]. Secondly, the limited buffer capacity of on-board switches and unpredictable queue delays (e.g., buffer overflow caused by burst traffic) significantly impair deterministic forwarding capabilities [9]. Therefore, directly applying traditional terrestrial time-sensitive network technologies to LEO satellite networks faces adaptability challenges. It is necessary to develop new scheduling algorithms that account for the dynamic topology and resource constraints of satellite networks to improve reliability, reduce packet loss rates, and enhance timeliness. Current research on resource scheduling for LEO satellite networks has made significant progress, with researchers significantly enhancing network transmission efficiency and QoS through innovative routing optimization and resource allocation strategies. For instance, in the context of STLs, [PERSON] et al. [10] proposed an integrated architecture for ultra-dense LEO networks and terrestrial networks, reducing end-to-end transmission latency through dynamic traffic scheduling. To address burst congestion in ISLs, a routing scheme [11] was developed that partitions the network into segments and directs traffic to low-load regions, effectively balancing link loads. Additionally, [PERSON] et al. [12] combined routing with a joint wavelength allocation mechanism for multi-wavelength beam ISLs, achieving maximum resource utilization while meeting stringent latency constraints. For end-to-end scenarios, the eTEG maximum flow routing algorithm [13], based on a time-expanded graph model, was proposed to optimize traffic transmission and energy efficiency. To meet the latency-sensitive requirements of diverse services, [PERSON] et al. [14] developed an AI-based multipath traffic scheduling method, modeling the problem as a Markov decision process to derive intelligent scheduling strategies. Similarly, [PERSON] et al. [15] propose a software-defined networking (SDN)-based architecture that employs deep reinforcement learning to address load-balancing challenges. These studies have improved network resource utilization but lack guarantees for high-priority services. For single-satellite performance optimization, [PERSON] et al. [16] proposed an optimization model to analyze queuing delay performance under limited buffer conditions, determining appropriate buffer sizes. [PERSON] et al.