When to compute in space

TL;DR

This paper presents a quantitative framework for optimizing compute location selection across space and ground resources, considering multiple constraints and incomplete information, to aid mission designers in space architecture planning.

Contribution

It introduces a formalized, measurable, and adaptable optimization model for compute placement in space missions, addressing the complexity of heterogeneous resource selection.

Findings

Framework effectively compares compute tiers and identifies optimal deployment locations.

Model accommodates incomplete information and multiple constraints.

Demonstrated on two representative workloads.

Abstract

Spacecraft increasingly rely on heterogeneous computing resources spanning onboard flight computers, orbital data centers, ground station edge nodes, and terrestrial cloud infrastructure. Selecting where a workload should execute is a nontrivial multi objective problem driven by latency, reliability, power, communication constraints, cost, and regulatory feasibility. This paper introduces a quantitative optimization framework that formalizes compute location selection through empirically measurable metrics, normalized scoring, feasibility constraints, and a unified utility function designed to operate under incomplete information. We evaluate the model on two representative workloads demonstrating how the framework compares compute tiers and identifies preferred deployment locations. The approach provides a structured, extensible method for mission designers to reason about compute placement in emerging space architectures.