Lightspeed Data Compute for the Space Era

2026-01-24

AI Summary

This work addresses the challenge of limited downlink bandwidth that prevents low Earth orbit (LEO) satellites from transmitting the vast volumes of remote sensing data they collect daily. To overcome this bottleneck, the authors propose SpaceCoMP, a MapReduce-style in-orbit collaborative processing framework tailored for inter-satellite laser link mesh networks. Its key innovation lies in integrating orbital dynamics into the computational model, enabling distance-aware routing and a bipartite graph matching–based task scheduling strategy to optimize task placement and minimize result aggregation overhead. Simulations with constellations of 1,000 to 10,000 satellites demonstrate that SpaceCoMP improves Map task placement efficiency by 61–79% over baseline approaches and outperforms greedy algorithms by 18–28%, while reducing aggregation costs by 67–72%.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• 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

While thousands of satellites photograph Earth every day, most of that data never makes it to the ground because downlink bandwidth simply cannot keep up. Processing data in the Low Earth Orbit (LEO) zone offers promising capabilities to overcome this limitation. We propose SpaceCoMP, a MapReduce-inspired processing model for LEO satellite mesh networks. Ground stations submit queries over an area of interest; satellites collect sensor data, process it cooperatively at light-speed using inter-satellite laser links, and return only the results. Our compute model leverages space physics to accelerate computations on LEO megaconstellations. Our distance-aware routing protocol exploits orbital geometry. In addition, our bipartite match scheduling strategy places map and reduce tasks within orbital regions while minimizing aggregation costs. We have simulated constellations of 1,000-10,000 satellites showcasing 61-79% improvement in map placement efficiency over baselines, 18-28% over greedy allocation, and 67-72% reduction in aggregation cost. SpaceCoMP demonstrates that the orbital mesh is not merely useful as a communication relay, as seen today, but can provide the foundations for faster data processing above the skies.

Problem

• downlink bandwidth
• satellite data
• LEO
• data processing
• orbital mesh

Innovation

• SpaceCoMP
• LEO satellite mesh
• in-orbit processing
• distance-aware routing
• bipartite task scheduling