Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks

2025-07-19

AI Summary

To address the scalability and real-time control challenges in Space-Air-Ground Integrated Networks (SAGIN) — specifically, the inefficiency of flat architectures in synchronizing massive satellite constellations and the low controller deployment efficiency under stringent resource constraints — this paper proposes Dora, a hierarchical domain architecture coupled with a reinforcement learning (RL)-based controller placement and resource allocation method. Dora innovatively integrates lightweight RL to jointly optimize controller location selection and resource assignment, balancing deployment quality and computational efficiency. Experimental results demonstrate that Dora improves configuration quality by 10% while reducing computation time to only 1/30–1/90 of conventional search algorithms. This breakthrough significantly alleviates the real-time and scalability bottlenecks inherent in resource-constrained, large-scale, dynamic SAGIN environments, establishing a new paradigm for efficient, adaptive network management.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Learning & Optimization for ROB
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

The rapid proliferation of satellite constellations in Space-Air-Ground Integrated Networks (SAGIN) presents significant challenges for network management. Conventional flat network architectures struggle with synchronization and data transmission across massive distributed nodes. In response, hierarchical domain-based satellite network architectures have emerged as a scalable solution, highlighting the critical importance of controller provisioning strategies. However, existing network management architectures and traditional search-based algorithms fail to generate efficient controller provisioning solutions due to limited computational resources in satellites and strict time constraints. To address these challenges, we propose a three-layer domain-based architecture that enhances both scalability and adaptability. Furthermore, we introduce Dora, a reinforcement learning-based controller provisioning strategy designed to optimize network performance while minimizing computational overhead. Our comprehensive experimental evaluation demonstrates that Dora significantly outperforms state-of-the-art benchmarks, achieving 10% improvement in controller provisioning quality while requiring only 1/30 to 1/90 of the computation time compared to traditional algorithms. These results underscore the potential of reinforcement learning approaches for efficient satellite network management in next-generation SAGIN deployments.

Problem

• Optimizing controller provisioning in hierarchical satellite networks
• Reducing computational overhead in satellite network management
• Improving scalability and adaptability of SAGIN architectures

Innovation

• Three-layer domain-based architecture enhances scalability
• Reinforcement learning optimizes network performance efficiently
• Dora reduces computation time significantly versus benchmarks