Intelligent Task Management via Dynamic Multi-region Division in LEO Satellite Networks

TL;DR

This paper introduces a dynamic multi-region division framework for LEO satellite networks that optimizes task management, routing, and resource utilization to reduce delay and improve efficiency in future 6G systems.

Contribution

It proposes a novel adaptive multi-region division algorithm and integrated routing and task offloading schemes tailored for large-scale LEO satellite networks.

Findings

Reduces task delay significantly

Improves energy efficiency per task

Enhances task completion rate

Abstract

As a key complement to terrestrial networks and a fundamental component of future 6G systems, Low Earth Orbit (LEO) satellite networks are expected to provide high-quality communication services when integrated with ground-based infrastructure, thereby attracting significant research interest. However, the limited satellite onboard resources and the uneven distribution of computational workloads often result in congestion along inter-satellite links (ISLs) that degrades task processing efficiency. Effectively managing the dynamic and large-scale topology of LEO networks to ensure balanced task distribution remains a critical challenge. To this end, we propose a dynamic multi-region division framework for intelligent task management in LEO satellite networks. This framework optimizes both intra- and inter-region routing to minimize task delay while balancing the utilization of computational and communication resources. Based on this framework, we propose a dynamic multi-region division algorithm based on the Genetic Algorithm (GA), which adaptively adjusts the size of each region based on the workload status of individual satellites. Additionally, we incorporate an adaptive routing algorithm and a task splitting and offloading scheme based on Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) to effectively accommodate the arriving tasks. Simulation results demonstrate that our proposed framework outperforms comparative methods in terms of the task delay, energy consumption per task, and task completion rate.