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

2025-07-14

AI Summary

To address low task processing efficiency, inter-satellite link congestion, and imbalanced resource loading in Low Earth Orbit (LEO) satellite networks, this paper proposes a dynamic multi-region collaborative scheduling framework. The framework integrates three key components: (1) a dynamic adaptive multi-region partitioning mechanism; (2) genetic algorithm (GA)-based regional topology reconfiguration; and (3) a multi-agent deep deterministic policy gradient (MA-DDPG)-driven task splitting and cross-domain offloading strategy. By jointly optimizing region partitioning, routing selection, and computational offloading, the framework enables tight coupling and coordinated management of communication and computing resources. Experimental results demonstrate that the proposed approach significantly outperforms baseline methods in terms of task latency, per-task energy consumption, and task completion rate. System resource utilization improves by 23.6%, validating the framework’s effectiveness and scalability in large-scale, highly dynamic LEO networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Balancing computational workloads in LEO satellite networks
• Optimizing intra- and inter-region routing to reduce task delay
• Managing dynamic large-scale topology for efficient task distribution

Innovation

• Dynamic multi-region division for task management
• Genetic Algorithm optimizes region size adaptively
• MA-DDPG based routing and task offloading