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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.14512 |
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| _version_ | 1866909708132024320 |
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| author | Peng, Qiyuan Zhang, Qi Gao, Yue Qiu, Kun |
| author_facet | Peng, Qiyuan Zhang, Qi Gao, Yue Qiu, Kun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14512 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks Peng, Qiyuan Zhang, Qi Gao, Yue Qiu, Kun Networking and Internet Architecture 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. |
| title | Dora: A Controller Provisioning Strategy in Hierarchical Domain-based Satellite Networks |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2507.14512 |