Towards a scalable design for survivable optical virtual private networks (O-VPNs)
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".