Die Oberfläche ist lokalisiert; Skill-Beschreibungen und -Dokumente werden per KI automatisch in Ihre Sprache übersetzt. Code, Befehle, Pfade und Fachbegriffe bleiben im Original, maßgeblich ist das Dokument des Autors.
Skills entdecken
Produktivität · SKILL.md

sla_recovery_evaluator

sla_recovery_evaluator

Compare fixed and optimized QoS allocations using delay/loss SLA satisfaction and mechanism checks for reduced recovery.

Independent GitHub snapshot · Automated classification · Original documentation governs

Skill-Dokumentation

GitHub

name: sla_recovery_evaluator description: Compare fixed and optimized QoS allocations using delay/loss SLA satisfaction and mechanism checks for reduced recovery.

SLA Recovery Evaluator

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 sla recovery evaluator 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.

Gespeicherte Skills