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

TL;DR

This paper introduces a workload-first framework for orbital data centers, emphasizing semantic abstraction to determine task suitability for space-based versus terrestrial cloud computing, supported by prototypes demonstrating significant data reduction.

Contribution

It proposes a novel workload-centric framework for orbital data centers, emphasizing semantic abstraction, with prototypes showing substantial data reduction for space-based processing.

Findings

Semantic reduction achieves up to 99.99% payload reduction.

Orbital data processing can significantly reduce data transfer needs.

Framework guides phased adoption of orbital data centers.

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.