Consideration for Space-Based Computing Infrastructure Network
draft-wang-space-computing-consideration-00

space                                                            J. Wang
Internet-Draft                                              China Mobile
Intended status: Informational                                  P. Zhang
Expires: 3 September 2026                             Beihang University
                                                            2 March 2026

     Consideration for Space-Based Computing Infrastructure Network
              draft-wang-space-computing-consideration-00

Abstract

   This document presents considerations for a Space-Based Computing
   Infrastructure Network from use cases and requirements.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions . . . . . . . . . . . . . . . . . . . . . . . . .   3
   3.  Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Emergency Response and Disaster Monitoring  . . . . . . .   3
     3.2.  Environmental Monitoring and Ecological Management  . . .   3
     3.3.  Deep Space Exploration Mission Support  . . . . . . . . .   4
     3.4.  In-orbit Training and Inference for Large AI Models . . .   4
   4.  Requirements  . . . . . . . . . . . . . . . . . . . . . . . .   4
     4.1.  Space-Based Computing Resource Monitoring . . . . . . . .   4
     4.2.  On-demand Traffic Scheduling  . . . . . . . . . . . . . .   5
   5.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .   5
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .   5
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   5
   8.  Informative References  . . . . . . . . . . . . . . . . . . .   5
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .   6

1.  Introduction

   In recent years, the global satellite industry has experienced rapid
   development.  The deployment of low-Earth orbit satellite
   constellations, advancements in satellite communication technologies,
   and improved space launch capabilities have propelled global
   satellite networks towards a more interconnected and intelligent
   system.  These developments have greatly improved the coverage,
   transmission speeds, system stability, and networking flexibility of
   satellite networks, allowing for seamless integration across air,
   land, and space domains.

   This increasingly mature global satellite network has broken the
   traditional constraints of space information transmission, resulting
   in more efficient inter-satellite and satellite-to-ground data
   exchange.  This has also laid a solid foundation for extending
   computing power into space.  On one hand, the stable and reliable
   satellite links provide efficient interconnection channels for
   computing facilities such as in-orbit computing, data processing, and
   intelligent sensing.  On the other hand, the widespread deployment of
   satellites has created opportunities for the distribution of
   computing nodes in space.

   This has led to the evolution of space computing power from isolated
   single-satellite operations to multi-satellite coordination, space-
   ground synergy, and global-scale orchestration.  This evolution is
   crucial in building space computing networks and achieving ubiquitous
   computing services across all domains.

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2.  Conventions

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in BCP
   14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.

3.  Use Cases

   Considering use cases on Space-Based Computing Infrastructure
   Network.

3.1.  Emergency Response and Disaster Monitoring

   During natural disasters, such as earthquakes and floods, traditional
   communication and computing systems are at risk of damage, resulting
   in delays in the transmission of critical information.  However, by
   utilizing satellite computing networks, emergency communication and
   computing nodes can be quickly deployed to process disaster imagery
   in real time.  This allows for the creation of precise disaster maps
   and optimal rescue routes, providing decision support at a minute or
   even second level.

   This greatly improves the efficiency of disaster warning, emergency
   response, and resource allocation.  Additionally, in the event of
   terrestrial network failures, these satellite networks can seamlessly
   provide communication and edge computing capabilities to support
   emergency command, drone search-and-rescue operations, and post-
   disaster reconstruction data processing.

3.2.  Environmental Monitoring and Ecological Management

   nder traditional models, large amounts of raw satellite data, such as
   0.3-meter high-resolution imagery, must be transmitted back to Earth
   for processing.  However, due to limited satellite-to-ground
   communication bandwidth, less than one-tenth of the data can be
   transmitted, resulting in low efficiency.

   To address this issue, AI models can be deployed in orbit to perform
   real-time target detection, classification, change monitoring, and
   feature extraction on remote sensing imagery.  This allows only
   critical analysis results to be transmitted to the ground, improving
   efficiency.  This technology can accurately identify farmland,
   forests, water bodies, and glaciers, making it easier to track carbon
   sinks, monitor water environments, and track vegetation degradation.

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   As a result, data utilization rates have increased from 10% to nearly
   100%, greatly enhancing the timeliness and autonomy of national land
   resource surveys, environmental monitoring, agricultural assessments,
   and related fields.

3.3.  Deep Space Exploration Mission Support

   Deep-space probes experience significant communication delays with
   Earth, with delays of several minutes being common for missions to
   Mars.  This reliance on ground control can be inefficient.However, by
   deploying computational nodes in deep-space orbits, these probes can
   perform in-orbit preprocessing, compression, and intelligent
   filtering of data.

   This allows for coordination through inter-satellite communication
   networks, resulting in a significant reduction in the volume of raw
   data that needs to be transmitted back to Earth.  This approach not
   only enhances the autonomous operation capabilities of probes, but
   also improves their mission response speed.  It serves as a critical
   foundation for future long-term exploration missions to destinations
   such as the Moon, Mars, and beyond.

3.4.  In-orbit Training and Inference for Large AI Models

   Training AI models with hundreds of billions of parameters requires
   immense computational power, which can pose energy and thermal
   bottlenecks for ground-based data centers.  However, by leveraging
   the distributed computing capabilities and green energy advantages of
   space computing networks, it is possible to distribute model training
   and inference.

   This approach provides a new "zero-carbon" computing pathway for AI
   development.

4.  Requirements

   Considering requirements on Space-Based Computing Infrastructure
   Network..

4.1.  Space-Based Computing Resource Monitoring

   Spaceborne equipment faces significant constraints in terms of
   computational resources, including CPU/GPU processing power, storage
   capacity, and energy consumption limits.  These limitations are due
   to the size, power consumption, and payload capacity of the
   equipment.  Additionally, the computational configurations of
   different satellites can vary greatly.  Some prioritize edge
   computing, while others focus on data relay.

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   Furthermore, the computational load of satellites can fluctuate
   depending on mission requirements.  For example, sudden spikes in
   remote sensing data processing or IoT terminal access within a
   specific region can overload local satellites, while satellites in
   other areas may remain idle.

   This highlights the need for a technical solution that can monitor
   the computational load, available resources, and energy consumption
   status of each satellite in real-time.  This data would then be used
   to support cross-satellite resource allocation.

4.2.  On-demand Traffic Scheduling

   Satellite networks support a wide range of service types, each with
   unique demands for network and computing power.  For example,
   emergency communications require low latency and high reliability,
   while remote sensing data processing requires significant computing
   power but is less sensitive to latency.  IoT data transmission
   prioritizes high bandwidth and low power consumption.

   However, a unified scheduling strategy may lead to issues such as
   "computing power mismatch" (e.g. assigning high-latency services to
   long-range satellites) or "resource wastage" (e.g. using high-
   performance computing satellites for simple data relay tasks).

   Therefore, it is crucial to establish a matching mechanism between
   service requirements and resource capabilities, including network
   resources such as link status, in order to enable efficient on-demand
   scheduling.

5.  Conclusion

   This document makes some considerations on Space-Based Computing
   Infrastructure Network.

6.  Security Considerations

   TBD.

7.  IANA Considerations

   TBD.

8.  Informative References

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   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

Authors' Addresses

   Jing Wang
   China Mobile
   No.32 XuanWuMen West Street
   Beijing
   100053
   China
   Email: wangjingjc@chinamobile.com

   Pengfei Zhang
   Beihang University
   No.37 Xueyuan Road, Haidian District
   Beijing
   100191
   China
   Email: zhangpengfei@buaa.edu.cn

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