Sdwan Flow Model

---
name: sdwan_flow_model
description: Represent flow groups, overlay links, priorities, SLA thresholds, and per-flow measurements for QoS optimization experiments.
---

# SD-WAN Flow and SLA Model

## When To Use
Use this skill when reconstructing or testing the SD-WAN QoS optimization mechanism from the paper. It is appropriate for reduced recovery experiments, validation fixtures, and future implementations that need the paper's sd-wan flow and sla model contract. Do not use it as evidence for full NS3 reproduction by itself.

## Inputs
- Scenario JSON with `links`, `flows`, and optional `measurements`.
- Flow records include `id`, `priority`, `demand`, `allowed_links`, `delay_sla`, and `loss_sla`.
- Link records include `id` and `capacity` in Mbps.

## Outputs
- Validated scenario objects, allocation dictionaries, SABE estimates, local-search traces, or SLA metrics depending on the entry point.
- JSON artifacts suitable for `recovery_result.json` mechanism checks.

## Workflow
1. Validate the SD-WAN flow and link model before optimization.
2. Estimate safe link capacity from passive measurements with the SABE helper when measurements are available.
3. Run priority-aware allocation before evaluating SLA satisfaction.
4. Preserve trace records showing high-priority reservation and low-priority search increments.
5. Report reduced/proxy limitations explicitly when not running packet-level simulation.

## Validation
Run `python scripts/sdwan_qos.py tests/fixture_scenario.json --output /tmp/sdwan_qos_result.json` or `python -m pytest tests` from the skill directory. The included tests use deterministic fixtures and do not require the original repository.

## Limitations
The scripts implement a compact mechanism-faithful proxy rather than the full nonlinear solver or NS3 simulator. Capacity units are Mbps and packet loss is represented as a fraction.