A Particle Swarm Optimization-Based Queue Scheduling and Optimization Mechanism for Large-Scale Low-Earth-Orbit...

Area of Science:

• Satellite Communication Networks
• Network Optimization
• Swarm Intelligence

Background:

• Large-scale low-Earth-orbit satellite networks exhibit dynamic spatial topology and variable node output port loads.
• Queue lengths and numbers at network nodes significantly impact packet loss and end-to-end latency for traffic flows.
• Optimizing queues is crucial for high-quality, deterministic transmission of time-sensitive traffic.

Purpose of the Study:

• To introduce a novel queue scheduling optimization mechanism (PSO-QSO) for dynamic satellite communication networks.
• To analyze traffic flow parameters and determine maximum time-sensitive loads within network nodes.
• To achieve deterministic end-to-end transmission performance for time-sensitive traffic.

Main Methods:

• Developed a queue scheduling optimization mechanism (PSO-QSO) tailored for low-Earth-orbit satellite networks.
• Analyzed relevant parameters of various traffic flows within the network.
• Applied the Particle Swarm Optimization algorithm to determine optimal queue lengths and numbers at each node's output port.

Main Results:

• The PSO-QSO mechanism effectively analyzes network traffic and node loads.
• Optimal queue configurations were calculated for forwarding time-sensitive traffic.
• The proposed mechanism ensures deterministic end-to-end transmission for time-sensitive data.

Conclusions:

• The PSO-QSO mechanism enhances the performance of large-scale low-Earth-orbit satellite communication networks.
• It provides reliable, real-time satellite communication services by optimizing queue management.
• This approach guarantees deterministic transmission crucial for time-sensitive applications.