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name: qos_local_search description: Allocate high-priority traffic first and iteratively increase low-priority shaper rates while residual estimated capacity permits objective improvement.

Priority-Aware QoS Local Search

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 priority-aware qos local search 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.