Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks

Area of Science:

• Network Engineering
• Satellite Communications
• Computer Science

Background:

• Low-Earth-Orbit (LEO) satellite networks face dynamic topology changes and resource limitations impacting inter-satellite links (ISLs).
• Traditional terrestrial Quality of Service (QoS) methods are inadequate for the unique challenges of LEO satellite environments, leading to performance degradation for time-sensitive flows (TSFs).
• Instability in LEO satellite networks, caused by frequent link changes and resource competition, hinders reliable transmission of time-sensitive data.

Purpose of the Study:

• To address the challenges of providing reliable QoS for TSFs in dynamic LEO satellite networks.
• To develop a novel management framework using software-defined networking (SDN) specifically designed for LEO satellite environments.
• To improve the efficiency and performance of time-sensitive traffic transmission in LEO satellite networks.

Main Methods:

• A software-defined networking (SDN) management framework tailored for LEO satellites was developed.
• An advanced queue management and scheduling system, inspired by terrestrial time-sensitive networking, was implemented.
• Differentiated forwarding strategies and priority-based classification were incorporated to enhance traffic management.

Main Results:

• The proposed framework significantly boosts overall network throughput.
• Packet loss was substantially reduced, improving data integrity.
• Low latency was maintained, crucial for time-sensitive applications.
• The system demonstrated optimized performance for time-sensitive traffic in LEO satellite networks.

Conclusions:

• The SDN-based management framework effectively tackles the dynamic nature and resource constraints of LEO satellite networks.
• The advanced queue management and scheduling system enhances the efficiency of transmitting time-sensitive traffic.
• The study validates the proposed approach through simulations, confirming its ability to improve network performance metrics.