orbital-compute 0.3.0

Simulate and schedule compute jobs across satellite constellations

Orbital Compute Simulator

The honest, open-source answer to "should we put GPUs in orbit?"

Live Demo | 3D Globe | Cost Calculator | Constellation Designer | Data Pipeline

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Everyone in orbital compute is selling a dream. We sell the math.

• Orbital compute is 24-50x more expensive than AWS. We publish the numbers.
• In-orbit processing saves 99% of downlink bandwidth. We prove it with simulation.
• Battery capacity fades to 66% in 3 years of LEO cycling. We model the physics.
• The PHOENIX algorithm improves worst-case battery by 23 percentage points. We implemented the paper.

This is the only open-source tool that models the complete space datacenter stack — from orbital mechanics to job scheduling to business economics — and tells you the truth about what works and what doesn't.

No hype. No hand-waving. No "trust us." Run the simulation yourself.

Who Is This For?

You are...	You need...	Start here
Startup founder pitching orbital compute to VCs	"Is this economically viable?"	python -m orbital_compute.cost_model (30 sec)
Grad student writing an OEC thesis	A baseline simulator to compare your algorithm against	pip install orbital-compute && python -m orbital_compute
Engineer at Starcloud/Axiom/Kepler	"Will PHOENIX scheduling help our battery life?"	python -m orbital_compute.phoenix
Mission planner sizing a constellation	"How many sats do I need for 1000 GPU-hours/day?"	python -m orbital_compute.designer
Journalist writing about space datacenters	"Is orbital compute actually viable?"	Data Pipeline Visualization
Conference reviewer checking an OEC paper	"Are these numbers realistic?"	REPRODUCIBILITY.md

pip install orbital-compute
python -m orbital_compute

Why In-Orbit Processing?

The data pipeline simulator proves the core value proposition:

Strategy                   Generated   Downlinked   Saved    Backlog
raw_downlink                 1042 GB      900 GB    13.6%    142 GB  (growing!)
image_classification         1042 GB       10 GB    99.0%      0 GB
object_detection             1042 GB        5 GB    99.5%      0 GB

Raw downlink can't keep up — 142 GB backlog growing every day. In-orbit processing saves 99% of bandwidth. This is why orbital compute exists.

What It Does

26 Python Modules

Category	Modules	What It Models
Orbital Mechanics	orbit.py, constellations.py	SGP4 propagation, TLE data, eclipse detection, real Starlink TLEs from CelesTrak
Power & Thermal	power.py, thermal.py	Solar panels, battery charge/discharge, Stefan-Boltzmann radiative cooling
Scheduling	scheduler.py, scheduler_v2.py	Greedy + look-ahead schedulers with eclipse forecasting (PHOENIX-inspired)
Networking	isl.py, network.py	Inter-satellite optical links, routing, topology analysis, Floyd-Warshall
Radiation	radiation.py	SEU fault injection, South Atlantic Anomaly, 4 recovery strategies
Workloads	workloads.py	9 realistic types: Earth obs, AI inference, defense/ISR, scientific
Ground Stations	ground_stations.py	10 global stations, elevation-angle contact windows, pass prediction
Data Pipeline	data_pipeline.py	Sensor → storage → in-orbit processing → downlink. Proves 99% bandwidth savings
Mission Planning	mission_planner.py	Ops timeline, conflict detection, data budget calculator, availability
Design	designer.py	Auto-design optimal constellation from requirements + budget
Economics	cost_model.py	CAPEX/OPEX, break-even, ROI, terrestrial comparison. Honest: 24-50x more expensive
Reliability	reliability.py	MTBF, constellation availability (binomial), SLA analysis, degradation curves
Debris & Propulsion	debris.py, propulsion.py	Collision avoidance, CAM planning, Kessler risk, drag model, station-keeping, deorbit
Federated Learning	federated.py	Train ML models across constellation (FedAvg/FedProx/SCAFFOLD), ISL-aware aggregation
K8s Integration	k8s_scheduler.py	Kubernetes scheduler extender for orbit-aware pod placement
Standards	formats.py, standards.py	CCSDS OEM, STK ephemeris, ECSS power/thermal/link budgets
Interface	api.py, cli.py, simulator.py, visualize.py	REST API, unified CLI, core engine, 3D globe generator

Interactive Web App

Live at shipitandpray.github.io/orbital-compute

• Dashboard — Configure constellation, run simulation in-browser, see fleet stats + timelines
• 3D Globe — Three.js visualization with orbits, ground stations, ISL links, play/pause
• Cost Calculator — Drag sliders, see CAPEX/OPEX/break-even update in real-time
• Constellation Designer — Input requirements, get optimal design with Walker geometry
• Data Pipeline — Canvas chart proving why in-orbit processing is necessary
• API Docs — REST endpoint documentation with curl examples

All runs in the browser. No server. No login. No cold starts.

Quick Start

git clone https://github.com/ShipItAndPray/orbital-compute.git
cd orbital-compute
pip install -r requirements.txt

# 30-second overview of everything
python -m orbital_compute

# Full demo (all subsystems: orbit + power + thermal + ISL + radiation + scheduling)
python demo.py

# Custom simulation
python run_sim.py --sats 12 --hours 24 --jobs 100

# Constellation designer (auto-design from requirements)
python -m orbital_compute.designer

# Cost analysis
python -m orbital_compute.cost_model

# Data pipeline comparison
python -m orbital_compute.data_pipeline

# Reliability/SLA analysis
python -m orbital_compute.reliability

# Mission planning timeline
python -m orbital_compute.mission_planner

# ECSS standards compliance
python -m orbital_compute.standards

# Industry format export (CCSDS OEM, STK)
python -m orbital_compute.formats

# Web dashboard
python dashboard.py

# REST API
python -m orbital_compute.api --port 8080

# Scheduler benchmark (greedy vs look-ahead)
python benchmark.py

# Fetch real Starlink TLEs
python -m orbital_compute.constellations

# Or use make shortcuts
make test        # Run 200+ tests
make demo        # Full demo
make cost        # Cost analysis
make pipeline    # Data pipeline proof
make serve       # Local web app

Key Results

Constellation Designer

Input:  1000 GPU-hours/day, global coverage, $50M budget
Output: 6 satellites, 8 GPUs each, 2000 km altitude
        $45.9M total (under budget), 1017 GPU-hours/day

Reliability Analysis

12-satellite constellation:
  99%    SLA: YES (1 operational needed, 11 spare)
  99.99% SLA: YES (1 operational needed)
  99.999% SLA: NO (need all 12 — not feasible with 1.4yr MTBF)

Cost Model (Honest Assessment)

Orbital compute: $86-172/GPU-hour (12 sats, Falcon 9)
AWS equivalent:  $4.10/GPU-hour
Ratio: 24-50x more expensive

But: processing in orbit saves 99% downlink bandwidth.
The value prop is NOT raw $/hr — it's avoiding the downlink bottleneck.

Full Simulation (12 sats, 12h, 80 jobs)

Jobs completed:     80/80
Fleet utilization:  32%
Eclipse fraction:   26-37% per satellite
Radiation SEUs:     303 events, all recovered via checkpoint-restart
ISL links:          6 active (mesh topology)
Temperature range:  -19°C to 24°C (within limits)

Architecture

orbital_compute/          # 26 Python modules
├── orbit.py              # SGP4 propagation, eclipse detection
├── power.py              # Solar + battery model
├── thermal.py            # Stefan-Boltzmann radiative cooling
├── scheduler.py          # v1: Greedy orbit-aware scheduler
├── scheduler_v2.py       # v2: Look-ahead with eclipse forecasting
├── ground_stations.py    # 10 stations, contact windows
├── isl.py                # Inter-satellite links, LOS, routing
├── network.py            # Topology analysis, Floyd-Warshall routing
├── radiation.py          # SEU injection, SAA, recovery strategies
├── workloads.py          # 9 workload types + mixed traffic generator
├── data_pipeline.py      # Sensor → processing → downlink pipeline
├── mission_planner.py    # Ops timeline, pass prediction, data budgets
├── designer.py           # Auto-design constellation from requirements
├── cost_model.py         # Full economics + terrestrial comparison
├── reliability.py        # MTBF, SLA, degradation curves
├── constellations.py     # Pre-defined configs + real TLE fetch
├── formats.py            # CCSDS OEM, STK ephemeris, JSON Schema
├── standards.py          # ECSS power/thermal/link budgets
├── debris.py             # Collision probability, CAM planning, Kessler risk
├── propulsion.py         # Atmospheric drag, station-keeping, deorbit planning
├── federated.py          # Federated learning across constellation (FedAvg/FedProx)
├── k8s_scheduler.py      # Kubernetes scheduler extender for orbit-aware pods
├── api.py                # REST API
├── simulator.py          # Core simulation engine
├── visualize.py          # 3D Three.js globe generator
└── cli.py                # Unified CLI

docs/                     # Web app (GitHub Pages)
├── index.html            # Landing page
├── dashboard.html        # Interactive simulator
├── globe.html            # 3D orbit visualization
├── cost.html             # Cost calculator
├── designer.html         # Constellation designer
├── pipeline.html         # Data pipeline analysis
└── api-docs.html         # API documentation

tests/                    # 107 tests, all passing

Research References

• PHOENIX (IEEE 2024) — Sunlight-aware task scheduling for energy-efficient space edge computing
• KubeSpace (arXiv 2601.21383, 2026) — Low-latency K8s control plane for LEO container orchestration
• Krios (USENIX HotEdge 2020) — Loosely-coupled orchestration for the LEO edge
• Comprehensive Survey of Orbital Edge Computing (Chinese Journal of Aeronautics, 2025)
• Axiom Space AxDCU-1 — First orbital data center on ISS (2025), Red Hat Device Edge
• Starcloud — First H100 GPU in orbit (Nov 2025), NVIDIA partnership
• ESA ASCEND — European orbital compute initiative, €300M through 2027

Contributing

See CONTRIBUTING.md. High-impact areas:

• Kubernetes scheduler plugin
• Real satellite hardware integration
• Multi-objective constellation optimization
• Federated learning orchestrator

License

MIT — see LICENSE