When to compute in space

2025-12-18

AI Summary

Spacecraft heterogeneous computing environments—comprising onboard computers, orbital data centers, ground-edge nodes, and cloud infrastructures—pose significant challenges in jointly optimizing latency, reliability, power consumption, communication overhead, cost, and regulatory compliance for task placement. Method: This work proposes a multi-objective dynamic task placement framework that integrates a normalized scoring model grounded in empirical metrics, feasibility constraints, and a unified utility function under partial information, enabling quantitative, cross-tier resource coordination. It employs multi-objective optimization combined with constraint-satisfaction reasoning. Contribution/Results: Evaluated on two representative spaceborne workloads, the framework enables quantitative performance comparison across tiers, precisely identifies optimal execution locations, and significantly improves task reliability and resource utilization efficiency while respecting operational and regulatory constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Optimizes compute location selection for spacecraft workloads
• Addresses multi-objective constraints like latency and reliability
• Provides a framework for decision-making under incomplete information

Innovation

• Optimizes compute location across space-ground infrastructure
• Uses quantitative metrics and unified utility function
• Provides structured framework for mission design decisions