Orbit Scheduler

Coming Q2 2026 — This is a design preview. Request early access 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:

Placement Engine

Decides which node should handle each workload based on constraints and current state

Orbit Model

Predicts satellite positions, ground contacts, and eclipse periods

Energy Model

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

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}")

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
  }
});

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

# 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:

# 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:

job = client.runtime.submit(
    model="...",
    prompt="...",
    placement={"prefer": "orbital", "fallback": "earth"}
)

Earth-First

Prefer Earth, use orbital for overflow:

job = client.runtime.submit(
    model="...",
    prompt="...",
    placement={"prefer": "earth", "fallback": "orbital"}
)

Latency-Optimized

Route to minimize latency to specific region:

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:

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

Getting Started

Planning Tools

Orbital Intelligence

CAE

Operator Agent

Mission Control

Orbital Runtime

Orbital Sim

Distributed Compute

SDKs

Resources

Orbit Scheduler

Orbit Scheduler

Overview

Key Capabilities

Architecture

Placement Engine

Orbit Model

Energy Model

API Preview

Submit Job with Placement Hints

Query Node Status

Schedule Future Work

Placement Strategies

Orbital-First

Earth-First

Latency-Optimized

Energy-Optimized

Scheduling Factors

Node Types

Next Steps

Adaptive Runtime

Resilient Compute

​Orbit Scheduler

​Overview

​Key Capabilities

​Architecture

Placement Engine

Orbit Model

Energy Model

​API Preview

​Submit Job with Placement Hints

​Query Node Status

​Schedule Future Work

​Placement Strategies

​Orbital-First

​Earth-First

​Latency-Optimized

​Energy-Optimized

​Scheduling Factors

​Node Types

​Next Steps

Adaptive Runtime

Resilient Compute

Orbit Scheduler

Overview

Key Capabilities

Architecture

API Preview

Submit Job with Placement Hints

Query Node Status

Schedule Future Work

Placement Strategies

Orbital-First

Earth-First

Latency-Optimized

Energy-Optimized

Scheduling Factors

Node Types

Next Steps