Eunomia: A Multicontroller Domain Partitioning Framework in Hierarchical Satellite Network

TL;DR

Eunomia is a novel domain-partitioning framework for hierarchical satellite networks that reduces latency, signaling overhead, and request loss by leveraging FOV-aware segmentation and optimized controller assignment.

Contribution

It introduces a movement-aware FOV segmentation method and a hybrid control plane with spectral clustering and Kuhn-Munkres algorithm for improved domain partitioning.

Findings

Reduces request loss by up to 58.3%

Decreases control overhead by up to 50.3%

Cuts algorithm execution time by 77.7%

Abstract

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.