Constella：一种面向低地球轨道太空数据中心的高成本效益分布式AI推理框架

中文摘要 AI 辅助

由低地球轨道（LEO）卫星星座构建的太空数据中心作为一种可扩展的计算基础设施正日益受到关注。凭借丰富的太阳能和高吞吐量的光学星间链路，此类星座可直接在轨道上运行AI工作负载，从而支持新型太空应用类型，同时优化地球观测等现有应用。然而，管理结合异构卫星角色的卫星星座带来了成本优化挑战。针对给定工作负载确定合适的星座规模和卫星角色比例颇具挑战性，因为过度配置处理卫星会增加系统成本，而配置不足则会限制系统效率。为了在此类太空数据中心中实现AI推理工作负载的高成本效益执行，我们提出了Constella，一种利用DNN拆分在LEO卫星星座中进行分布式AI推理的新颖框架。Constella包含一个离线资源识别器，用于确定处理器卫星与通信卫星的最优比例，以及一个在线分配算法。该算法利用星座遥测数据，在星座内部及向地面站自适应地路由数据。我们在真实世界卫星数据集上，针对复杂度递增的场景对Constella进行了评估。结果表明，与其他方法相比，系统成本降低了最多两个数量级，端到端推理延迟降低了最多2.7倍，同时保持了不低于81.9%的推理成功率。

英文摘要

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 2.7x compared to other approaches, while maintaining no less than 81.9% inference success rate.

发表机构

• Technische Universität Wien（维也纳工业大学）

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