一句话：提出轨道边缘计算系统 Kodan，通过专用 ML 模型生成与运行时动态选择，在计算瓶颈下最大化下行数据价值。
低轨卫星星座的部署成本下降，但卫星高速运动、图像数据量大且地面站接触时间短，造成严重的数据下行瓶颈。轨道边缘计算在太空边缘过滤数据可缓解下行压力，却将挑战转移到星载计算能力受限的问题上。Kodan 是一个轨道边缘计算系统，分两阶段工作：一次性变换步骤利用卫星数据分析应用的参考实现和代表性数据集，生成面向太空边缘部署的专用 ML 模型；部署后，运行时系统在计算瓶颈约束下为每个数据样本动态选择最佳专用模型，以最大化下行传输的有价值数据。通过过滤低价值数据并优先传输高价值数据，Kodan 在饱和下行链路上将数据价值密度提升 89% 至 97%。
Decreasing costs of deploying space vehicles to low-Earth orbit have fostered an emergence of large constellations of satellites. However, high satellite velocities, large image data quantities, and brief ground station contacts create a data downlink challenge. Orbital edge computing (OEC), which filters data at the space edge, addresses this downlink bottleneck but shifts the challenge to the inelastic computational capabilities onboard satellites. In this work, we present Kodan: an OEC system that maximizes the utility of saturated satellite downlinks while mitigating the computational bottleneck. Kodan consists of two phases. A one-time transformation step uses a reference implementation of a satellite data analysis application, along with a representative dataset, to produce specialized ML models targeted for deployment to the space edge. After deployment to a target satellite, a runtime system dynamically selects the best specialized models for each data sample to maximize valuable data downlinked within the constraints of the computational bottleneck. By intelligently filtering low-value data and prioritizing high-value data for transmit via the saturated downlink, Kodan increases the data value density between 89 and 97 percent.
@inproceedings{paperbot10080,
title = {Kodan: Addressing the Computational Bottleneck in Space},
author = {Bradley Denby and Krishna Chintalapudi and Ranveer Chandra and Brandon Lucia and Shadi Noghabi},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3582016.3582043}
}