Which Workloads Belong in Orbit? A Workload-First Framework for Orbital Data Centers Using Semantic Abstraction
This addresses the challenge of efficiently allocating space-based compute resources for data-intensive AI workloads, though it appears incremental in applying existing semantic techniques to a new orbital context.
The paper tackles the problem of determining which AI workloads should be processed in orbital data centers versus terrestrial clouds, proposing a workload-centric framework. It demonstrates that Earth-observation pipelines can achieve 99.7-99.99% payload reduction and stereo reconstruction reduces data from ~306 MB to ~1.57 MB (99.49% reduction), showing semantic abstraction drives suitability.
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.