Constella: A Novel Framework for Cost-Efficient Distributed AI Inference in LEO Space Data Centers

Abstract

Space data centers built from Low-Earth Orbit (LEO) satellite constellations are gaining increasing attention as a scalable computing infrastructure. With access to abundant solar energy and high-throughput optical inter-satellite links, such constellations can run AI workloads directly in orbit, enabling new in-space application types while optimizing existing ones such as Earth observation. However, managing satellite constellations that combine heterogeneous satellite roles introduces a cost optimization challenge. Determining the appropriate constellation size and satellite role ratio for a given workload is challenging, as over-provisioning processing satellites increases system cost, while under-provisioning limits system efficiency. To enable cost-efficient execution of AI inference workloads in such space data centers, we present Constella, a novel framework that leverages DNN splitting for distributed AI inference in LEO satellite constellations. Constella comprises an offline resource identifier that determines the optimal ratio of processor-to-communicator satellites and an online assignment algorithm. The algorithm utilizes constellation telemetry to adaptively route data within the constellation and to ground stations. We evaluate Constella on a real-world satellite dataset across scenarios of increasing complexity. Results demonstrate a reduction in system cost by up to two orders of magnitude and lower end-to-end inference latency by up to 2.7 compared to other approaches, while maintaining no less than 81.9% inference success rate.