Towards a scalable design for survivable optical virtual private networks (O-VPNs)
Why this work is in the frame
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Bibliographic record
Abstract
The paper tackles the resource allocation problem for optical networks supporting virtual private networks (O-VPNs), in which working and spare capacities are allocated in the networks for satisfying a series of traffic matrices corresponding to each O-VPN. Based on the (M:N)/sup n/ protection architecture defined in generalized multiprotocol label switching (GMPLS), we propose two novel integer linear program (ILP) models, namely ILP-I and ILP-II, aiming to initiate a graceful compromise between the capacity efficiency and computation time without losing the ability of addressing QoS requirements in each O-VPN. Experiment results show that, in terms of capacity efficiency, a significant improvement is achieved by ILP-I compared to ILP-II at the expense of higher computation time. Although ILP-II is outperformed by ILP-I, it can handle the situation with an arbitrary size of O-VPNs. We conclude that the proposed ILP models yield a scalable solution for capacity planning in survivable optical networks supporting O-VPNs based on the (M:N)/sup n/ protection architecture.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it