Eunomia: A Multicontroller Domain Partitioning Framework in Hierarchical Satellite Network

With the rise of mega-satellite constellations, the integration of hierarchical non-terrestrial and terrestrial networks has become a cornerstone of 6G coverage enhancements. In these hierarchical satellite networks, controllers manage satellite switches within their assigned domains. However, the high mobility of LEO satellites and field-of-view (FOV) constraints pose fundamental challenges to efficient domain partitioning. Centralized control approaches face scalability bottlenecks, while distributed architectures with onboard controllers often disregard FOV limitations, leading to excessive signaling overhead. LEO satellites outside a controller’s FOV require an average of five additional hops, resulting in a 10.6-fold increase in response time. To address these challenges, we propose Eunomia, a three-step domain-partitioning framework that leverages movement-aware FOV segmentation within a hybrid control plane combining ground stations and MEO satellites. Eunomia reduces control plane latency by constraining domains to FOV-aware regions and ensures single-hop signaling. It further balances traffic load through spectral clustering on a Control Overhead Relationship Graph and optimizes controller assignment via the Kuhn-Munkres algorithm. We implement Eunomia on the Plotinus emulation platform with realistic constellation parameters. Experimental results demonstrate that Eunomia reduces request loss by up to 58.3%, control overhead by up to 50.3%, and algorithm execution time by 77.7% significantly outperforming current state-of-the-art solutions.

💡 Research Summary

The paper “Eunomia: A Multicontroller Domain Partitioning Framework in Hierarchical Satellite Network” addresses a critical challenge in the emerging era of mega-constellations for 6G non-terrestrial networks (NTN). The core problem is efficiently partitioning low-earth orbit (LEO) satellites into manageable control domains under the constraints of controller field-of-view (FOV) and the high mobility of LEO satellites. Existing approaches, whether centralized (ground station-based), fully distributed (LEO-based), or hierarchical (MEO/GEO-based), suffer from scalability bottlenecks, resource constraints on satellites, excessive latency, and most importantly, fail to adequately integrate FOV limits with satellite mobility patterns. This leads to frequent control topology reconfigurations, unstable domain boundaries, and significantly inflated control overhead—LEOs outside a controller’s FOV require an average of five extra hops, causing a 10.6x increase in response time.

To overcome these limitations, the authors propose Eunomia, a novel three-step domain partitioning framework operating within a unified hybrid control plane that combines ground stations (GS) and medium-earth orbit (MEO) satellites. The framework’s workflow begins with a Graph Preprocessing Module. This module gathers network topology for the current stable time slot and uses the traffic distribution from the previous slot (leveraging temporal continuity of ground traffic patterns) to construct a Control Overhead Relationship Graph (CORG). The CORG quantitatively models total control overhead—including flow table updates, domain synchronization, controller migration, and path computation—and represents the control cost relationship between any two LEO satellites as edge weights.

The core innovation lies in the Domain Partitioning Module, which executes a three-step algorithm on the CORG. First, it identifies initial candidate domains based on the FOV coverage of each controller (GS or MEO). Second, it handles LEO satellites that reside within the overlapping FOV regions of multiple controllers. Here, Eunomia introduces its key advancement: “Movement-aware FOV partitioning.” Instead of using simple geometric proximity, it groups satellites based on their predictable orbital mechanics, such as belonging to the same orbital plane and moving in the same direction (e.g., northbound). This creates groups that are more likely to stay together over time. Spectral clustering is then applied to these movement-aware groups using the CORG edge weights, forming clusters that maximize intra-domain cohesion and minimize inter-domain coupling.

Third, the framework solves the optimal assignment problem, matching the formed clusters (domains) with the available controllers using the Kuhn-Munkres algorithm. This step considers controller processing capacity and estimated domain load to achieve global load balancing across the hybrid control plane.

The authors implemented and evaluated Eunomia on the Plotinus emulation platform using realistic constellation parameters from operational systems like Starlink and OneWeb. Experimental results demonstrate that Eunomia significantly outperforms state-of-the-art solutions. It reduces request loss rates by 19.6% to 58.3%, cuts control overhead by 19.95% to 50.3%, and remarkably reduces the algorithm’s own execution time by 77.7%, proving its practicality for real-time, large-scale operations. In conclusion, Eunomia presents a paradigm shift by proactively leveraging the predictability of orbital dynamics for stable and efficient control plane design, offering a robust solution for the scalable management of future hierarchical satellite networks.