> ## Documentation Index
> Fetch the complete documentation index at: https://docs.rotastellar.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Orbit Scheduler
> Workload orchestration across Earth and orbital nodes
# Orbit Scheduler
**Coming Q2 2026** — This is a design preview.
[Request early access](https://rotastellar.com/developers) to be notified when available.
## Overview
The Orbit Scheduler orchestrates workloads across heterogeneous compute nodes spanning Earth datacenters and orbital infrastructure. It understands orbital mechanics, energy availability, and network topology to make optimal placement decisions.
## Key Capabilities
* **Orbit-aware scheduling** — Accounts for orbital position, eclipse periods, ground contacts
* **Energy optimization** — Routes work based on power availability
* **Latency-aware routing** — Minimizes round-trip time based on geometry
* **Fault tolerance** — Automatic failover between nodes
* **Workload splitting** — Distribute work across Earth + orbit
## Architecture
The Orbit Scheduler uses three models to make placement decisions:
Decides which node should handle each workload based on constraints and current state
Predicts satellite positions, ground contacts, and eclipse periods
Tracks power availability, battery state, and solar input across all nodes
**Available Nodes:**
| Node | Location | Characteristics |
| ------------------ | ------------ | ------------------------------------------ |
| Earth DC (us-west) | Terrestrial | Always available, lowest latency to US |
| LEO-1 | 550 km orbit | Solar-powered, intermittent ground contact |
| LEO-2 | 550 km orbit | Solar-powered, different orbital plane |
## API Preview
### Submit Job with Placement Hints
```python Python theme={null}
from rotastellar import RotaStellarClient
client = RotaStellarClient(api_key="rs_...")
job = client.runtime.submit(
model="llama-70b",
prompt="...",
placement={
"prefer": "orbital", # Prefer orbital nodes
"fallback": "earth", # Fall back to Earth if needed
"region_affinity": "europe", # Prefer nodes with Europe visibility
"max_hops": 2 # Max ISL hops
},
constraints={
"latency_sla_ms": 100,
"energy_budget_wh": 0.5
}
)
print(f"Placed on: {job.node}")
print(f"Reason: {job.placement_reason}")
```
```typescript Node.js theme={null}
import { RotaStellarClient } from '@rotastellar/sdk';
const client = new RotaStellarClient({ apiKey: 'rs_...' });
const job = await client.runtime.submit({
model: 'llama-70b',
prompt: '...',
placement: {
prefer: 'orbital',
fallback: 'earth',
regionAffinity: 'europe',
maxHops: 2
},
constraints: {
latencySlams: 100,
energyBudgetWh: 0.5
}
});
```
```bash cURL theme={null}
curl -X POST https://api.rotastellar.com/v1/runtime/jobs \
-H "Authorization: Bearer rs_your_api_key" \
-H "Content-Type: application/json" \
-d '{
"model": "llama-70b",
"prompt": "...",
"placement": {
"prefer": "orbital",
"fallback": "earth",
"region_affinity": "europe",
"max_hops": 2
},
"constraints": {
"latency_sla_ms": 100,
"energy_budget_wh": 0.5
}
}'
```
### Query Node Status
```python theme={null}
# Get current node availability
nodes = client.runtime.nodes()
for node in nodes:
print(f"{node.id}: {node.type}")
print(f" Location: {node.location}")
print(f" Status: {node.status}")
print(f" Available power: {node.available_power_w}W")
print(f" Queue depth: {node.queue_depth}")
if node.type == "orbital":
print(f" In eclipse: {node.in_eclipse}")
print(f" Ground contact: {node.has_ground_contact}")
```
### Schedule Future Work
Schedule jobs to run at optimal times:
```python theme={null}
# Schedule for optimal conditions
scheduled_job = client.runtime.schedule(
model="llama-70b",
prompt="...",
schedule={
"window_start": "2026-01-22T00:00:00Z",
"window_end": "2026-01-22T12:00:00Z",
"optimize_for": "energy" # or "latency", "cost"
}
)
print(f"Scheduled for: {scheduled_job.scheduled_time}")
print(f"Expected node: {scheduled_job.expected_node}")
print(f"Energy savings: {scheduled_job.energy_savings_percent}%")
```
## Placement Strategies
### Orbital-First
Prefer orbital nodes, fall back to Earth:
```python theme={null}
job = client.runtime.submit(
model="...",
prompt="...",
placement={"prefer": "orbital", "fallback": "earth"}
)
```
### Earth-First
Prefer Earth, use orbital for overflow:
```python theme={null}
job = client.runtime.submit(
model="...",
prompt="...",
placement={"prefer": "earth", "fallback": "orbital"}
)
```
### Latency-Optimized
Route to minimize latency to specific region:
```python theme={null}
job = client.runtime.submit(
model="...",
prompt="...",
placement={
"optimize_for": "latency",
"user_location": {"lat": 51.5, "lon": -0.1} # London
}
)
```
### Energy-Optimized
Route to nodes with best energy availability:
```python theme={null}
job = client.runtime.submit(
model="...",
prompt="...",
placement={"optimize_for": "energy"}
)
```
## Scheduling Factors
The scheduler considers:
| Factor | Weight | Description |
| ------------------- | ------ | --------------------------- |
| Energy availability | High | Current and predicted power |
| Latency | High | Network path to user |
| Queue depth | Medium | Current load on node |
| Thermal headroom | Medium | Temperature margin |
| Eclipse status | Medium | Upcoming power constraints |
| Ground contact | Low | Communication availability |
## Node Types
| Type | Location | Characteristics |
| ------- | ------------------ | ------------------------------------------- |
| `earth` | Terrestrial DC | Unlimited power, stable network |
| `leo` | Low Earth Orbit | Variable power, intermittent ground contact |
| `meo` | Medium Earth Orbit | Stable power, higher latency |
| `geo` | Geostationary | Continuous visibility, 240ms+ latency |
## Next Steps
Learn about energy-aware inference
Learn about fault tolerance