面向空天地海一体化网络的动态任务与资源调度

中文摘要 AI 辅助

在6G泛在连接的背景下，空天地海一体化网络（SAGSIN）作为一种普适服务供给的新范式应运而生。为支持基础设施稀缺的海洋区域中不断扩大的海事活动，我们提出了一种面向SAGSIN的创新动态任务与资源调度方法，为船舶提供计算服务。该方法整合了广覆盖卫星、具备中继能力的高空平台（HAP）、能量充足的沿海基站（BS）以及灵活部署的无人机（UAV），以适应广域、高移动性和持续性的海事服务。为应对跨四层任务调度的挑战，我们开发了一种动态任务卸载算法。该算法基于实时系统状态，将任务流导向负载较轻、计算能力强且链路速率高的服务器，以降低任务执行延迟，并集成了一种前瞻性卫星切换策略，以缓解切换后的拥塞并提高卫星资源利用率。考虑到无人机续航能力有限，我们施加剩余能量约束，以确保任务积压交接和安全返航。此外，无人机-基站带宽分配、无人机轨迹和计算资源分配被联合优化，以增强低空设备间的连通性并加速任务完成。仿真结果验证了所提方法在复杂海事环境中任务执行期间对系统资源变化的卓越适应性，与基准方法相比，平均任务延迟至少降低了23%。

英文摘要

In the context of 6G ubiquitous connectivity, the space-air-ground-sea integrated network (SAGSIN) emerges as a new paradigm for pervasive service provisioning. To support expanding maritime activities in infrastructure-scarce ocean areas, we propose an innovative dynamic task and resource scheduling approach for SAGSIN to deliver computing services for vessels. It integrates broad-coverage satellites, relay-capable high-altitude platform (HAP), energy-sufficient coastal base station (BS), and flexibly deployed uncrewed aerial vehicles (UAVs) to accommodate wide-area, highly mobile, and sustained maritime services. To address the challenge of task scheduling across four layers, a dynamic task offloading algorithm is developed. It steers task flows toward servers with light loads, strong computing capabilities, and high-rate links based on real-time system states to reduce task execution delay, integrating an anticipatory satellite handover strategy to mitigate post-handover congestion and improving satellite resource utilization. Considering the limited endurance of UAVs, we impose residual energy constraints to ensure task backlog handover and safe return. Furthermore, the UAV-BS bandwidth allocation, UAV trajectories, and computing resource allocation are jointly optimized to enhance the connectivity among low-altitude devices and accelerate task completion. Simulation results validate the proposed method's superior adaptability to system resource variations during task execution in complex maritime environments, achieving at least a 23% reduction in average task delay over benchmarks.

发表机构

• The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））

机构由 AI 辅助整理，请以论文原文为准。