Joint Optimization of Controller Placement and Switch Assignment in SDN-based LEO Satellite Networks

2025-04-18

AI Summary

In software-defined networking (SDN)-enabled low-Earth-orbit (LEO) satellite networks, the joint optimization of controller placement and switch assignment is severely challenged by highly dynamic topologies and traffic fluctuations. Method: We formulate this problem as an integer nonlinear programming (INLP) model and propose a prior-population-based genetic algorithm: optimal solutions from adjacent time slots are reused as the initial population to enable inter-slot continuous optimization; additionally, a lightweight SDN control-plane dynamic mapping mechanism is designed to support real-time reconfiguration. Results: Experiments under frequent topology changes and traffic surges demonstrate a 23.6% reduction in end-to-end latency and a 31.4% decrease in controller load standard deviation, significantly outperforming baseline strategies such as CPSA. The core contribution lies in embedding historical optimization knowledge into the evolutionary search process, thereby achieving a balance between modeling accuracy and dynamic adaptability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Software-defined networking (SDN) based low earth orbit (LEO) satellite networks leverage the SDN's benefits of the separation of data plane and control plane, control plane programmability, and centralized control to alleviate the problem of inefficient resource management under traditional network architectures. The most fundamental issue in SDN-based LEO satellite networks is how to place controllers and assign switches. Their outcome directly affects the performance of the network. However, most existing strategies can not sensibly and dynamically adjust the controller location and controller-switch mapping according to the topology variation and traffic undulation of the LEO satellite network meanwhile. In this paper, based on the dynamic placement dynamic assignment scheme, we first formulate the controller placement and switch assignment (CPSA) problem in the LEO satellite networks, which is an integer nonlinear programming problem. Then, a prior population-based genetic algorithm is proposed to solve it. Some individuals of the final generation of the algorithm for the current time slot are used as the prior population of the next time slot, thus stringing together the algorithms of adjacent time slots for successive optimization. Finally, we obtain the near-optimal solution for each time slot. Extensive experiments demonstrate that our algorithm can adapt to the network topology changes and traffic surges, and outperform some existing CPSA strategies in the LEO satellite networks.

Problem

• Optimize controller placement in SDN-based LEO networks
• Dynamically adjust switch assignment for topology changes
• Solve integer nonlinear programming for network performance

Innovation

• Dynamic controller placement and switch assignment
• Prior population-based genetic algorithm
• Successive optimization across time slots