Infrastructure Design 📡 Amazon Leo & AWS Architectures for Software-Defined Private Satellite Networking ☁️

A Builder's Lifecycle: From Design to Sustainment

• Another transport and edge platform alongside Outposts, Local Zones, and Wavelength—just one that happens to operate from orbit.
• A way to plug remote or space-based assets directly into your AWS-native architectures without rewriting everything for "satellite mode."

Understanding the Leo Data Path

1. Customer device → Customer terminal
   A laptop, router, or industrial controller connects to a Leo Nano/Pro/Ultra terminal.
2. Terminal → LEO satellite
   The terminal’s phased-array antenna talks to a passing satellite.
3. LEO satellite → OISL mesh
   Data may hop across multiple satellites using optical inter-satellite links.
4. Satellite → Ground Gateway → POP
   Once in view of a ground gateway, data is downlinked and handed to a POP.
5. POP → Amazon backbone
   Traffic enters Amazon’s global fiber network.
6. Backbone → Internet or AWS
   Traffic either:
   • Exits to the public internet, or
   • Stays on the private backbone and is delivered Direct to AWS (D2A) into a VPC.

Customer Device → Customer Terminal → LEO Satellite → OISL → Ground Gateway → POP → Amazon Backbone → Internet/AWS Cloud

Customer Virtual Network (CVN) and Virtual Network Interfaces (VNI)

• Customer-managed network segmentation:
  Create private IP ranges, subnets, and DNS settings.
• Use cases:
  • Separate control-plane and data-plane traffic.
  • Split employee internet from operations data.
  • Insert custom routing (firewalls, inspection, NVA appliances).

• A VNI (Virtual Network Interface) is the key attachment point:
  • Represents the endpoint for a given antenna or terminal.
  • Typically, one VNI per CVN per antenna (1:1 mapping).
• A CVN Gateway:
  • One per CVN.
  • Connects the CVN to external destinations:
    • Internet
    • Direct to AWS (D2A)
    • Private Network Interconnect (PNI)

• Its own gateway and antenna(s).
• Separate VNIs and segmentation policies.
• Distinct routing outcomes (e.g., one CVN for internet-only, one for private D2A).

Pattern 1: Space-to-Cloud with CVN + VNI

• LEO terminal (at a remote site) connects to:
• LEO satellite, which relays to:
• Ground Gateway → POP, which feeds:
• Amazon Leo environment → CVN → VNI, then:
• Out to either Internet or AWS.

• Apply your own routing, firewalling, or NAT—just like you would in a VPC.
• Segment workloads into multiple subnets for traffic isolation.
• Attach multiple VNIs and treat each antenna as a separate network entry point.

Pattern 2: Hybrid via Private Network Interconnect (PNI)

• CVN / Amazon Leo side:
  • CVN with subnets and VNIs.
  • An antenna symbol at the boundary indicates the satellite link.
  • A connection point on the CVN edge connects to PNI.
• PNI:
  • A dedicated, private, secure circuit between Leo’s edge and your:
• Corporate Data Center:
  • Servers, storage, and databases behind traditional firewalls.

• You want Leo as another branch in your MPLS/SD-WAN fabric—treating satellite links like any other private circuit.
• You need tight control over routing, identity, and data residency (think: finance, healthcare, defense).
• You want to avoid the public internet entirely for certain applications—keeping traffic on your own wires (and lasers) end-to-end.
• You need tight control over routing, identity, and data residency.
• You want to avoid the public internet entirely for certain applications.

Pattern 3: Direct to AWS (D2A) – Leo ↔ VPC

• Customer Virtual Network with:
  • Subnet(s)
  • VNI(s)
• Edge/input devices feeding data:
  • Security cameras
  • IoT/sensors
  • Robotics/industrial devices
  • Mobile or handheld units

• VPC hosting:
  • EC2 for compute
  • RDS for relational data
  • SageMaker for ML training/inference
    The VNI in the CVN is directly linked (via D2A) to your VPC networking—no NAT, no internet gateway, no hops through the public web.

• Edge analytics where preprocessing happens near the terminal (say, on a ship or oil rig), but core ML inference and storage live in AWS.
• Use cases that need private, predictable latency from remote sites into cloud services—think real-time dashboards or control loops.
• Scenarios where you want to apply your existing VPC security patterns (NACLs, Security Groups, IAM) directly to Leo traffic, treating it like any other VPC attachment.
• Use cases that need private, predictable latency from remote sites into cloud services.
• Scenarios where you want to apply your existing VPC security patterns (NACLs, SGs, IAM) to Leo traffic.

Space IoT and Predictive Workloads (Builder View)

1. Ingest:
   Use Amazon Kinesis to ingest telemetry (orbital updates, device readings, health data).
2. Process:
   Use AWS Lambda for transformations and quick checks.
   Persist state in Amazon DynamoDB if needed.
3. Orchestrate:
   Drive longer workflows with AWS Step Functions.
   Spawn EC2 jobs when you need GPUs, custom libraries, or longer runtimes.
4. Store & Search:
   Archive in Amazon S3.
   You can apply this pattern to:

• Ships at sea with intermittent connectivity windows
• Aircraft that only downlink telemetry during certain flight phases

Digital Twins in Remote Locations (Wind Farm Example)

• Amazon ECS in a VPC runs the digital twin model.
• AWS IoT Core ingests turbine telemetry.
• Amazon Timestream stores time-series performance data.
• Amazon S3 holds general data, model artifacts, and historical logs.

• Anomaly trigger → AWS Lambda:
  Detects anomalies in turbine behavior.
  Sends metrics to Amazon Managed Grafana for visualization.
• Action trigger → AWS Lambda:
  Issues control signals (e.g., “slow down” or “halt” turbines) based on the twin’s outputs.
  This pattern generalizes to any industrial asset in a hard-to-reach area:
• Mines for predictive maintenance on excavation equipment
• Offshore oil rigs for real-time anomaly detection and safety shutdowns

Egress Inspection for Sensitive Workloads

• Amazon Leo CVN + VNI:
  Provides secure, low-latency, private connectivity from the wind farm to the VPC.
• Subnet VNI at the wind farm:
  Handles local routing for turbines and edge gateways.
  Forwards data across the Leo link without touching the public internet.

• Remote site (AWS Outposts rack):
  Hosts a VPC with a private subnet and EC2 instances.
  Result:
• All egress from on-prem EC2 (and potentially Leo-connected workloads) is forced through centralized cloud-based inspection before reaching the internet.
• You get full visibility and control over what leaves your network—even if the workload is running on an Outposts rack in a remote facility connected via Leo.

Edge Video Analytics with Leo and AWS

1. EC2 on Outposts initiates outbound (egress) traffic.
2. Traffic routes back to the AWS Region via Outposts.
3. In-region, it enters a VPC and flows:
   • From the antenna endpoint (representing the Outposts link),
   • To Firewall Endpoints,
   • Through Network Firewall for inspection.
4. If allowed, traffic is forwarded to the Internet Gateway and out to the public internet.

• All egress from on-prem EC2 (and potentially Leo-connected workloads) is forced through centralized cloud-based inspection before reaching the internet.

• A security camera streams video to an antenna.
• Data flows into an Amazon Leo edge environment:
  This pattern is applicable to:
• Smart cities and public safety: Traffic monitoring, crowd detection, and emergency response coordination.
• Industrial inspection and quality assurance: Automated defect detection on assembly lines or construction sites.
• Retail analytics in locations with poor terrestrial connectivity: Store traffic analysis, shelf monitoring, and inventory management.
• Wildlife and environmental monitoring: Camera traps in remote areas feeding into ML models for species identification and conservation planning.

Security, Identity, and Observability for Builders

• AWS Step Functions orchestrate multi-step analysis workflows.
• AWS Lambda performs frame-by-frame or event-driven processing.
• Amazon OpenSearch Service indexes metadata and events (e.g., detected objects, anomalies).
• Amazon API Gateway exposes APIs to retrieve analysis results.
• AWS Amplify hosts front-end applications for Ops users to view live dashboards, alerts, and historical queries.

• Industrial inspection and quality assurance.
• Retail analytics in locations with poor terrestrial connectivity.

Conclusion: Amazon Leo is . . .

• Built on AWS: Using the same compute, networking, and security services you already deploy—Step Functions, Kinesis, SageMaker, IAM, the works.
• Integrated with your account: Connecting to edge locations, VPCs, and on-premises data centers via CVN, D2A, and PNI—treating satellite links like any other network attachment.
• Ready for builders: With proven patterns for Space IoT, digital twins, edge AI, egress inspection, video analytics, and more.

• [0]: Intro to Amazon Leo 
• [200]: How AWS Brought Project Kuiper to Market Check out the talk: