# Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
## 3 Problem Modeling
### Routing Algorithm
Experimental results demonstrate that the proposed algorithm significantly enhances network resource utilization, increases the throughput of time-sensitive services, and reduces packet loss rates, while maintaining low algorithm complexity and shorter execution times in multi-task environments. Therefore, the algorithm is well suited for scenarios requiring high real-time data processing, such as telemedicine, intelligent transportation, and the Internet of Things (IoT), particularly under high-load conditions, where it prioritizes the transmission of time-sensitive services and optimizes network resource utilization. However, challenges related to computational resources, storage space, and energy consumption in large-scale satellite network environments may impact the practical deployment of this algorithm. First, dynamic topology management in large-scale networks significantly increases signaling overhead, leading to higher bandwidth consumption and a greater risk of network congestion. The Clustered Multi-Criteria Routing (CMCR) Algorithm proposed in the literature [34] optimizes routing efficiency and load balancing in large-scale LEO constellations through clustering and multi-criteria routing, demonstrating advantages in reducing signaling overhead and computational complexity. In contrast, the algorithm presented in this paper exhibits advantages in global optimization and path flexibility, enabling the assurance of time-sensitive services and overall load balancing under high-load conditions. Nevertheless, further improvements are needed in controlling signaling overhead. Additionally, the limited storage capacity in satellite networks may affect the ability to cache path and link information in large-scale networks, thereby reducing the responsiveness of path optimization. Moreover, the battery capacity of 200-300 watt-hours restricts satellite operations under high-frequency path computation scenarios. Specifically, LEO satellites are typically equipped with low-power processors (e.g., ARM Cortex-A series) and limited memory (500 MB to 1 GB), which may constrain the frequent link weight adjustments required by the algorithm, ultimately affecting the network's real-time responsiveness. Although this study employs strategies such as time-slot division, lightweight graph-based routing algorithms, and VPN-based implementation schemes to mitigate resource consumption, it has not fully addressed the detailed computation, storage, and energy capabilities of individual satellites, nor has it implemented fine-grained resource scheduling. These limitations highlight areas for future optimization and necessitate further research and refinement in practical deployments. ## 7 Conclusions

This paper addresses the challenge of balancing load distribution and ensuring the quality of service (QoS) for time-sensitive traffic in satellite networks by proposing a multi-criteria routing algorithm based on the spatiotemporal distribution of service traffic. The algorithm prioritizes low-latency paths for time-sensitive services and optimizes link weight allocation, significantly improving network resource utilization under high-load conditions while maintaining the service quality for both time-sensitive and non-time-sensitive traffic. Experimental results demonstrate that, compared to traditional algorithms, the proposed method increases the throughput of time-sensitive traffic by up to 20.8% and reduces its packet loss rate by up to 76.8%, effectively enhancing overall network resource utilization. Although the performance difference in terms of packet loss rate and throughput for time-sensitive traffic is minor compared to existing optimization methods, the proposed algorithm exhibits significantly reduced path planning time under the same number of tasks, demonstrating superior computational efficiency.