Inter-Domain Path Provisioning with Security Features: Architecture and Signaling Performance
Bibliographic record
Abstract
Significant research and standardization efforts are underway to enable automated computation and reservation of connection-oriented paths (circuits) across multiple domains. In the absence of a secure authentication and authorization mechanism, however, carriers continue to provision connections manually, which leads to large setup delays and increases possibility of configuration errors. Carriers also lack mechanisms to meter connection quality during the service lifetime and typically do not exchange accounting information for established connections for auditing and billing purposes. In this paper, we address the challenge for automatic multi-domain path provisioning with authentication, authorization and accounting (AAA) capabilities in carrier-grade transport networks. The designed solution secures computation and reservation for path provisioning and also leverages a standard accounting model which incorporates the accounting signaling for an inter-domain connection. In order to evaluate the impact of the proposed framework on signaling performance, we also provide an analytical framework scalable to large inter-domain network scenarios. We verify the analysis using event-driven simulations and then use this analytical model to quantify the feasibility of our model in terms of signaling load and signaling delay for a wide range of network scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".