STAR-GS：面向地面站即服务的可信且感知可见性的资源调度
STAR-GS: Truthful and Visibility-Aware Resource Scheduling for Ground Station as a Service
浏览论文内容
中文总结 AI 辅助
针对GSaaS资源调度的NP难问题，本文提出STAR-GS机制，结合拍卖理论与可调度性分析，在仿真中其收益、性能及扩展性均优于基线。
中文摘要 AI 辅助
低地球轨道卫星星座的快速增长催生了对高效可扩展下行链路服务日益增长的需求。地面站即服务（GSaaS）为卫星运营商提供按需接入模式，但商业GSaaS提供商必须在多颗具有异构数据需求、重叠可见窗口、严格截止期限和策略性竞价行为的卫星之间调度有限的地面站带宽。本文从以地面站为中心的视角研究GSaaS资源调度问题，提供商需共同决定任务接纳、地面站分配、带宽分配和支付。在卫星轨道动态、带宽约束和下行链路任务截止期限下，最大化提供商收益是NP难问题。为应对这一挑战，本文提出STAR-GS，一种可信且感知可行性的调度机制，其结合了感知竞价的接纳控制、最佳适配地面站分配、基于最早截止期限优先（EDF）的带宽调度以及关键支付定价。通过将拍卖理论与可调度性分析相结合，STAR-GS激励任务所有者如实报告其私有估值，同时确保已接纳的任务能在截止期限前可行完成。使用Ansys系统工具包（STK）进行的仿真显示，STAR-GS始终比启发式基线实现更高收益，获得接近混合整数线性规划（MILP）的性能且运行时显著更低，能平稳扩展至包含多达900个任务的工作负载。
英文摘要
The rapid growth of Low Earth Orbit satellite constellations has created increasing demand for efficient and scalable downlink services. Ground Station as a Service (GSaaS) provides an on-demand access model for satellite operators, but commercial GSaaS providers must schedule limited ground-station bandwidth among multiple satellites with heterogeneous data demands, overlapping visibility windows, strict deadlines, and strategic bidding behaviors. This paper studies GSaaS resource scheduling from a ground-station-centric perspective, where the provider jointly determines task admission, ground-station assignment, bandwidth allocation, and payments. Under satellite orbital dynamics, bandwidth constraints, and downlink task deadlines, maximizing the provider's revenue is NP-hard. To address this challenge, we propose STAR-GS, a truthful and feasibility-aware scheduling mechanism that combines bid-aware admission control, best-fit ground-station assignment, Earliest Deadline First (EDF)-based bandwidth scheduling, and critical-payment pricing. By integrating auction theory with schedulability analysis, STAR-GS incentivizes task owners to truthfully report their private valuations while ensuring that admitted tasks can be feasibly completed before their deadlines. Simulations using Ansys Systems Tool Kit (STK) show that STAR-GS consistently achieves higher revenue than heuristic baselines, obtains near-MILP performance with substantially lower runtime, and scales smoothly to workloads containing up to 900 tasks.
