Space Microdatacenters

Earth observation (EO) has been a key task for satellites since the first time a satellite was put into space. The temporal and spatial resolution at which EO satellites take pictures has been increasing to support space-based applications, but this increases the amount of data each satellite generates. We observe that future EO satellites will generate so much data that this data cannot be transmitted to Earth due to the limited capacity of communication that exists between space and Earth. We show that conventional data reduction techniques such as compression [130] and early discard [54] do not solve this problem, nor does a direct enhancement of today’s RFbased infrastructure [136, 153] for space-Earth communication. We explore an unorthodox solution instead - moving to space the computation that would have happened on the ground. This alleviates the need for data transfer to Earth. We analyze ten non-longitudinal RGB and hyperspectral image processing Earth observation applications for their computation and power requirements and discover that these requirements cannot be met by the small satellites that dominate today’s EO missions. We make a case for space microdatacenters - large computational satellites whose primary task is to support in-space computation of EO data. We show that one 4KW space microdatacenter can support the computation need of a majority of applications, especially when used in conjunction with early discard. We do find, however, that communication between EO satellites and space microdatacenters becomes a bottleneck. We propose three space microdatacenter-communication co-design strategies – 𝑘 − 𝑙𝑖𝑠𝑡-based network topology, microdatacenter splitting, and moving space microdatacenters to geostationary orbit that alleviate the bottlenecks and enable effective usage of space microdatacenters.

自首颗卫星进入太空以来，地球观测（Earth Observation, EO）一直是其关键任务之一。为了支持各类天基应用，EO卫星成像的时间和空间分辨率在不断提高，但这也增加了每颗卫星生成的数据量。

我们观察到， 未来的EO卫星将产生海量数据，由于天地（space-Earth）通信容量有限，这些数据将无法完全传输回地球。

我们证明， 传统的数据削减技术，如压缩 [130] 和早期丢弃 [54]，无法解决这一问题， 直接增强现有的基于射频（RF）的天地通信基础设施 [136, 153] 也同样无效。

为此，我们探索了一种非常规的解决方案 —— 将原本在地面进行的计算任务转移至太空。这减轻了将数据传输回地球的需求。

我们分析了十种非时序性的RGB与高光谱图像处理地球观测应用，评估了它们的计算和功率需求，并发现当今主导EO任务的小型卫星无法满足这些需求。

因此，我们提出 建设太空微数据中心（space microdatacenters）的构想——这是一种大型计算卫星，其主要任务是支持EO数据的在轨计算。

我们证明，一个4千瓦（KW）的太空微数据中心，特别是与早期丢弃技术结合使用时，可以支持大多数应用的计算需求。

然而，我们确实发现， EO卫星与太空微数据中心之间的通信 成为了一大瓶颈。

我们提出了三种太空微数据中心与通信的协同设计策略

• 基于 \(k-list\) 的网络拓扑
• 微数据中心拆分
• 将太空微数据中心移至地球静止轨道

这些策略可以缓解通信瓶颈，并实现对太空微数据中心的高效利用。

Introduction

The ability to launch satellites into space and then control them to accomplish a wide variety of tasks such as navigation [56], communication [64], forecasting [119], early warning [111], reconnaissance [107], broadcasting [106], scientific research [46], signals intelligence [109, 154], weapons delivery [73], and Earth observation [127] has been one of the most wondrous achievements of humankind. These satellites have different volumes (0.01 m 3 to 916 m 3 ) and weights (1.26 kg to 420 000 kg) and are placed into outer space at different altitudes above the Earth (274 km to 35 786 km) in different orbits (low Earth orbit [47], geostationary orbit [142], sunsynchronous orbit (SSO) [34], etc.) using launch vehicles [38, 50]. These satellites have different sources of power generation (none - for passive satellites [126], solar panels [123], radioisotopic thermoelectric generators [122], etc.) to support their functionality, use transponders [55] for communication to Earth-based ground stations [95], and work either alone or together as a group (often called a constellation [147]).

Earth observation (EO) has been a key task for satellites since inception. EO satellites image the Earth using camera [127], radar [67], lidar [116], photometer [140], or atmospheric instruments [36] in order to support a variety of scientific [12], military [109, 154], and commercial [55] applications. As imaging satellites, they are often placed in low Earth orbit for high data resolution (though some EO satellites are placed in a geostationary orbit [142] for uninterrupted coverage or in a SSO for consistent lighting during imaging [11]), and transmit their images to Earth-based ground stations for further processing. Following Sputnik-1 [117], the first satellite ever launched, thousands of EO satellites have been placed in space to support different applications [102]. A vast number of future satellite launches are also devoted to Earth observation [94] to support a fast growing Earth observation industry [94].

A key parameter for an EO satellite is the resolution at which it takes its pictures. Increasingly Earth observation space missions are being planned with aggressive goals of spatial and temporal resolution (Section 3) to support emerging EO applications such as forest fire detection [148], realtime video [134], conflict zone monitoring [28], tasking [40], warning systems for early responders [156], and tracking of events such as Earthquakes [159], hurricanes [51], and tornadoes [43], as well as objects such as aircraft [77] and missiles [31]. Even traditional EO applications such as flood monitoring [155], traffic monitoring [86], mapping [44], etc., seek higher resolutions requirements now. Mapping a narrow path in a dense urban area easily requires sub-meter resolution [158], for example. Fig. 2 shows how spatial resolution of EO satellites has improved over the decades.

将卫星发射到太空并控制它们完成导航 [56]、通信 [64]、天气预报 [119]、早期预警 [111]、侦察 [107]、广播 [106]、科学研究 [46]、信号情报 [109, 154]、武器投送 [73] 以及地球观测 [127] 等多种任务，是人类最奇妙的成就之一。这些卫星的体积（0.01 \(m^3\) 至 916 \(m^3\)）和重量（1.26 kg 至 420,000 kg）各不相同，通过运载火箭 [38, 50] 被部署在地球上空不同高度（274 km 至 35,786 km）的不同轨道上（如低地球轨道 [47]、地球静止轨道 [142]、太阳同步轨道（SSO）[34] 等）。这些卫星拥有不同的能源生成方式（无源卫星 [126] 无需能源，其他则使用太阳能电池板 [123]、放射性同位素热电发生器 [122] 等）来支持其功能；它们使用转发器 [55] 与地面站 [95] 进行通信，并以单颗或星座（constellation）[147] 的形式协同工作。

地球观测（EO）自卫星诞生之初便是一项核心任务。EO卫星使用相机 [127]、雷达 [67]、激光雷达 [116]、光度计 [140] 或大气探测仪器 [36] 对地球进行成像，以支持各种科学 [12]、军事 [109, 154] 和商业 [55] 应用。作为成像卫星，它们通常被部署在低地球轨道以获得高数据分辨率（尽管一些EO卫星为实现不间断覆盖而被部署在地球静止轨道 [142]，或为在成像期间获得一致的光照条件而被部署在太阳同步轨道 [11]），并将其图像传输到地面站进行后续处理。继第一颗人造卫星“斯普特尼克1号”（Sputnik-1）[117] 之后，已有数千颗EO卫星被送入太空以支持不同应用 [102]。未来大量的卫星发射计划也致力于地球观测 [94]，以支持快速增长的地球观测产业 [94]。

EO卫星的一个关键参数是其成像分辨率。越来越多的地球观测太空任务正以极高的空间和时间分辨率为目标（第3节）来支持新兴的EO应用，例如森林火灾探测 [148]、实时视频 [134]、冲突区域监控 [28]、任务规划 [40]、应急响应预警系统 [156]，以及对地震 [159]、飓风 [51]、龙卷风 [43] 等事件和飞机 [77]、导弹 [31] 等目标的追踪。即便是传统的EO应用，如洪水监测 [155]、交通监控 [86]、地图测绘 [44] 等，现在也寻求更高的分辨率。例如，在密集的城市区域绘制一条狭窄的路径，就需要亚米级的解决方案 [158]。图2展示了EO卫星的空间分辨率在过去几十年中的提升历程。

In this paper, we observe that the amount of data that future high resolution Earth observation satellites will generate will be so massive that data cannot simply be transmitted to the Earth considering present or projected ground station capacity (Section 3). The limited number of ground stations on the Earth limit the total amount of data that can be transmitted. At current costs, the monetary cost of transmission will also be prohibitive (Section 3).

We first evaluate two techniques (Section 4) that have been previously proposed to reduce the amount of data transmitted to the Earth - compression [130] and early discard [54] - to address the problem (Fig. 1b). We show that compression or early discard may not provide sufficient data reduction for many high resolution space missions either alone or in conjunction. We also consider (Section 4) if today’s RF-based communication infrastructure can be enhanced to support high resolution space missions. We show that practical RF-based satellite antennas may not support the needs for many such missions. The number of channels needed to be supported on the ground may also be unrealistic.

We explore an unorthodox solution instead (Section 5) - whenever possible, move the computation that would have happened on the ground to space itself. If we are able to perform the computation in space itself, only insights, not raw sensor data, may need to be transmitted to the ground alleviating the need for massive data transfer to the ground for high resolution applications.

在本文中，我们观察到，未来的高分辨率地球观测卫星将产生极其庞大的数据量，以至于在现有或预期的地面站容量下，这些数据根本无法被完全传输回地球（第3节）。地球上有限的地面站数量限制了可传输的总数据量。以目前的成本计算，传输的经济成本也将是令人望而却步的（第3节）。

我们首先评估了两种先前为减少数据传输量而提出的技术（第4节）——压缩 [130] 和早期丢弃 [54]——以解决该问题（图1b）。

我们证明，无论是单独使用还是结合使用，压缩或早期丢弃技术对于许多高分辨率太空任务而言，可能都无法提供足够的数据削减量。我们还考虑了（第4节）是否可以增强当今基于射频（RF）的通信基础设施来支持高分辨率太空任务。我们指出，实用的星载射频天线可能无法满足许多此类任务的需求，并且地面所需支持的信道数量也可能不切实际。

因此，我们转而探索一种非常规的解决方案（第5节）—— 在可能的情况下，将原本在地面进行的计算任务转移至太空本身。如果我们能够在太空完成计算，那么只需将分析得出的洞察（insights）而非原始传感器数据传输回地面， 从而为高分辨率应用减轻了海量数据传输的需求。

We analyze ten emerging non-longitudinal RGB and hyperspectral image processing Earth observation applications that process high resolution satellite data. We estimate for these applications their computation and power requirements at different resolutions. We find that small satellites which dominate Earth observation today, cannot support many of these applications, especially at high resolutions, as these satellites cannot generate enough power to support the power requirements of these applications. While early discard helps reduce the power requirements, the reduction is not enough to support many of these applications.

With the above in mind, we make a case for space microdatacenters (SµDCs) for high resolution Earth observation space missions (Section 6). A SµDC (Fig. 1c) is a relatively large computational satellite whose primary task is to support in-space computation on data generated by the observation satellites. The power generation capability for the SµDC is commensurate with the amount of computation supported by the SµDC. Inter-satellite links (ISLs) are used to offload the data generated by the observation satellites to the SµDC.

我们分析了十种处理高分辨率卫星数据的新兴非时序性RGB与高光谱图像处理地球观测应用，并估算了它们在不同分辨率下的计算和功率需求。我们发现，当今主导地球观测的小型卫星无法支持其中许多应用，尤其是在高分辨率下，因为这些卫星无法产生足够的电力来满足这些应用的功率需求。虽然早期丢弃技术有助于降低功率需求，但其削减量仍不足以支持其中许多应用。

基于以上发现，我们为高分辨率地球观测太空任务提出了建设太空微数据中心（SµDCs）的构想（第6节）。

SµDC（图1c）是一种相对大型的计算卫星，其主要任务是支持对观测卫星生成的数据进行在轨计算。 SµDC的发电能力与其所支持的计算量相匹配。 观测卫星生成的数据通过星间链路（Inter-satellite links, ISLs）卸载到SµDC

We consider the SµDC requirements for a 64-satellite constellation of Earth observation satellites for 4KW SµDCs based on NVIDIA RTX 3090-class processors. We show (Section 6) that one 4 kW SµDC can support the computation needs for a majority of our applications for most resolutions, especially when used in conjunction with early discard.

We do find, however, that communication between the observation satellites and the SµDCs becomes a bottleneck (Section 7). We propose three SµDC-communication co-design strategies – 𝑘 −𝑙𝑖𝑠𝑡based network topology, SµDC splitting, and moving SµDCs to geostationary orbit – to alleviate this bottleneck and effectively use these SµDCs (Section 8). Finally, we analyze the impact of placement and chip architecture on SµDC design and performance.

我们以一个由64颗地球观测卫星组成的星座为例，考虑了 基于NVIDIA RTX 3090级别处理器的4千瓦SµDC的需求。

我们证明（第6节），一个4千瓦的SµDC，特别是与早期丢弃技术结合使用时，可以满足我们大部分应用在多数分辨率下的计算需求。

然而，我们确实发现，观测卫星与SµDCs之间的通信成为了一个瓶颈（第7节）。我们提出了三种SµDC与通信的协同设计策略——基于 \(k-list\) 的网络拓扑、SµDC拆分以及将SµDCs移至地球静止轨道——以缓解这一瓶颈，并有效利用这些SµDCs（第8节）。最后，我们分析了部署位置和芯片架构对SµDC设计与性能的影响。

This paper makes the following contributions:

• We show that future high resolution Earth observation missions will generate so much data that the generated data cannot be transmitted to the Earth considering present or projected ground station capacity or considering the transmission costs.

• We show that compression, early discard, or antenna scaling have limited effectiveness at addressing the problem.

• We explore moving the Earth-based computation that computes on EO data into space and show that this computation cannot be performed on the typically small EO satellites since these satellites cannot meet the corresponding power requirements.

• We make a quantitative case for SµDCs that are designed to run the Earth-based computation in space. We show that a 4 kW SµDC can support a majority of the applications if communication bottlenecks can be alleviated.

• We present multiple SµDC-communication co-design strategies (new connection topologies, SµDC splitting, moving SµDCs to geostationary orbit) that alleviate the communication bottlenecks of SµDCs.

本文做出以下贡献:

• 我们证明，未来的高分辨率地球观测任务将产生海量数据，以至于在现有或预期的地面站容量或传输成本下，这些数据无法被传输回地球
• 我们证明，压缩、早期丢弃或天线规模扩展在解决此问题上的效果有限
• 我们探索了将处理EO数据的地面计算任务转移至太空的可能性，并证明这些计算任务无法在通常较小的EO卫星上执行，因为这些卫星无法满足相应的功率需求
• 我们为SµDCs（旨在太空运行地面计算任务）的建设提供了定量论证。我们证明，如果能够缓解通信瓶颈，一个4千瓦的SµDC可以支持大多数应用
• 我们提出了多种SµDC与通信的协同设计策略（新的连接拓扑、SµDC拆分、将SµDCs移至地球静止轨道），以缓解SµDCs的通信瓶颈