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Which Workloads Belong in Orbit? A Workload-First Framework for Orbital Data Centers Using Semantic Abstraction

Durgendra Narayan Singh

Abstract

Space-based compute is becoming plausible as launch costs fall and data-intensive AI workloads grow. This paper proposes a workload-centric framework for deciding which tasks belong in orbit versus terrestrial cloud, along with a phased adoption model tied to orbital data center maturity. We ground the framework with in-orbit semantic-reduction prototypes. An Earth-observation pipeline on Sentinel-2 imagery from Seattle and Bengaluru (formerly Bangalore) achieves 99.7-99.99% payload reduction by converting raw imagery to compact semantic artifacts. A multi-pass stereo reconstruction prototype reduces ~306 MB to ~1.57 MB of derived 3D representations (99.49% reduction). These results support a workload-first view in which semantic abstraction, not raw compute scale, drives early workload suitability.

Which Workloads Belong in Orbit? A Workload-First Framework for Orbital Data Centers Using Semantic Abstraction

Abstract

Space-based compute is becoming plausible as launch costs fall and data-intensive AI workloads grow. This paper proposes a workload-centric framework for deciding which tasks belong in orbit versus terrestrial cloud, along with a phased adoption model tied to orbital data center maturity. We ground the framework with in-orbit semantic-reduction prototypes. An Earth-observation pipeline on Sentinel-2 imagery from Seattle and Bengaluru (formerly Bangalore) achieves 99.7-99.99% payload reduction by converting raw imagery to compact semantic artifacts. A multi-pass stereo reconstruction prototype reduces ~306 MB to ~1.57 MB of derived 3D representations (99.49% reduction). These results support a workload-first view in which semantic abstraction, not raw compute scale, drives early workload suitability.
Paper Structure (30 sections, 1 equation, 5 figures, 8 tables)

This paper contains 30 sections, 1 equation, 5 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. Paper Organization
  4. Related Work
  5. First-Principles Constraints of Space-Based Compute
  6. Latency and Connectivity
  7. Bandwidth Asymmetry
  8. Energy Availability
  9. Reliability, Maintenance, and Security
  10. Workload Suitability Matrix
  11. Purpose of the Matrix
  12. Evaluation criteria and Scoring approach
  13. Workload Suitability Matrix
  14. Phased Capability Roadmap for Orbital Compute
  15. Phase 1: GPU Compute with Intermittent Downlink
  16. ...and 15 more sections

Figures (5)

  • Figure 1: Space--ground connectivity scenarios and latency drivers.
  • Figure 2: Bandwidth symmetry (illustrative).
  • Figure 3: Cross-region semantic abstraction across cloud regimes (Seattle vs Bengaluru, 2025).
  • Figure 4: Example EO semantic artifacts (Seattle 2025).
  • Figure 5: Multi-pass geometric abstraction pipeline (Los Angeles).