Earth-observation satellites are emerging as distributed edge platforms for time-critical tasks, yet orbital scheduling remains challenged by intermittent energy harvesting and temporal coupling where eager execution risks future battery depletion. Existing schedulers rely on static priorities and lack mechanisms to adaptively shed work. We present Equinox, a lightweight, decentralized runtime for resource-constrained orbital systems. Equinox enables adaptive scheduling by compressing time-varying constraints, including battery charge, thermal headroom, and queue backlog, into a single state-dependent marginal cost of execution. Derived from a barrier function that rises sharply near safety limits, this cost encodes both instantaneous pressure and future risk. This local signal serves as a constellation-wide coordination primitive. Tasks execute only when their value exceeds the current cost, enabling value-ordered load shedding without explicit policies. If local costs exceed a neighbor's, tasks are dynamically offloaded over inter-satellite links, achieving distributed load balancing without routing protocols or global state. We evaluate Equinox using a multi-day simulation of a 143-satellite constellation grounded in physical Jetson Orin Nano measurements. Equinox improves scientific goodput by 20% and image-processing throughput by 31% over priority-based scheduling while maintaining 2.2x higher mean battery reserves. Under high demand, Equinox achieves 5.2x the execution rate of static scheduling by gracefully shedding work rather than collapsing under contention.

翻译：地球观测卫星正逐渐成为执行时间关键型任务的分布式边缘平台，然而轨道调度仍面临间歇性能量采集和时间耦合的挑战——急切执行可能耗尽未来电池电量。现有调度器依赖静态优先级，缺乏自适应负载卸载机制。我们提出Equinox，一种面向资源受限轨道系统的轻量级分散式运行时环境。Equinox通过将时变约束（包括电池电量、热余量和队列积压）压缩为单一的状态依赖边际执行成本，实现自适应调度。该成本源自接近安全边界时急剧上升的屏障函数，同时编码瞬时压力与未来风险，作为星座级协调基元。任务仅在其价值超过当前成本时执行，无需显式策略即可实现价值排序的负载卸载。若本地成本超过邻居节点成本，任务将通过星间链路动态迁移，在无路由协议或全局状态的情况下实现分布式负载均衡。我们基于物理Jetson Orin Nano测量值，对143颗卫星星座进行多日仿真评估。相较于优先级调度，Equinox将科学有效吞吐量提升20%，图像处理吞吐量提升31%，同时平均电池储备保持2.2倍优势。在高负载条件下，Equinox通过优雅的负载卸载而非竞争崩溃，实现静态调度5.2倍执行速率。