Kodan: Addressing the Computational Bottleneck in Space

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.

将航天器部署至低地球轨道的成本不断降低，催生了大型卫星星座的出现。然而，卫星的高速度、海量的图像数据以及与地面站的短暂接触时间，共同构成了数据下行链路的严峻挑战。轨道边缘计算（Orbital Edge Computing, OEC）通过在空间边缘过滤数据来应对此下行链路瓶颈，但这也将挑战转移至卫星自身固有的、缺乏弹性的计算能力上。

在本文中，我们提出了Kodan：一个旨在最大化饱和卫星下行链路效用，同时缓解计算瓶颈的轨道边缘计算系统。Kodan包含两个阶段:

1. 首先，一个一次性的转换步骤会利用卫星数据分析应用的参考实现和一个代表性数据集，来生成一系列专门用于空间边缘部署的机器学习模型
2. 在部署到目标卫星后，一个运行时系统会为每个数据样本动态选择最优的专用模型，以便在计算能力受限的条件下，最大化下行传输的高价值数据

通过智能过滤低价值数据，并优先通过饱和的下行链路传输高价值数据，Kodan将数据价值密度提升了89%至97%

Note

一眼看上去，系统的构型特别特别常见：

1. 先给个相对合理的“初始态”
2. 后续每一步，在前面的基础上，动态变化 --> Optimize