qos_local_search
Allocate high-priority traffic first and iteratively increase low-priority shaper rates while residual estimated capacity permits objective improvement.
SKILL.mdbasicagent-skill/skill_library/paperbench/rice/skill/statemask_rl_explanation/qos_local_search/SKILL.md
GitHubPriority-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 optionalmeasurements. - Flow records include
id,priority,demand,allowed_links,delay_sla, andloss_sla. - Link records include
idandcapacityin 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.jsonmechanism checks.
Workflow
- Validate the SD-WAN flow and link model before optimization.
- Estimate safe link capacity from passive measurements with the SABE helper when measurements are available.
- Run priority-aware allocation before evaluating SLA satisfaction.
- Preserve trace records showing high-priority reservation and low-priority search increments.
- 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.