Equinox: Decentralized Scheduling for Hardware-Aware Orbital Intelligence

2026-04-21

AI Summary

This study addresses the scheduling challenges faced by Earth observation satellites under intermittent energy availability and tightly coupled task timing. The authors propose a lightweight, decentralized runtime scheduling mechanism that encodes time-varying constraints—such as battery charge, thermal margins, and queue backlogs—into state-dependent marginal execution costs. Innovatively, they introduce a barrier-function-based local cost signal as a swarm coordination primitive, enabling value-driven load balancing and graceful overload shedding without reliance on global state information or routing protocols. Experimental results from a 143-satellite simulation demonstrate a 20% increase in scientific effective throughput, a 31% improvement in image processing throughput, a 2.2-fold rise in average battery reserves, and a 5.2× higher task execution rate under high load compared to static scheduling.

Technology Category

• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

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.

Problem

• orbital scheduling
• energy harvesting
• adaptive load shedding
• resource-constrained systems
• time-critical tasks

Innovation

• decentralized scheduling
• hardware-aware
• marginal cost of execution
• load shedding
• orbital intelligence