Survivable routing and wavelength assignment (RWA) in optical virtual private networks (O-VPNs)
Bibliographic record
Abstract
This paper tackles the resource allocation problem for wavelength division multiplexing (WDM) networks supporting virtual private networks (O-VPNs), in which working and spare capacity are allocated in the networks for satisfying a series of traffic matrices corresponding to a group of O-VPNs. Based on the (M:N)/sup n/ protection architecture where multiple protection groups (PGs) are supported in a single network domain, we propose two novel integer linear programming (ILP) models, namely ILP-I and ILP-II, aiming to initiate a graceful compromise between the capacity efficiency and computation complexity without losing the ability of addressing the QoS requirements in each O-VPN. ILP-I optimizes the task of resource allocation by taking each O-VPN as a PG, while the ILP-II breaks down each O-VPN into multiple small PGs where all the working paths in each PG are mutually link-disjointedly routed. Experiment results show that in terms of capacity efficiency, a significant improvement can be achieved by ILP-I compared to that by ILP-II at the expense of much longer 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-II model yields a scalable solution for the capacity planning in the survivable optical networks supporting O-VPNs based on the (M:N)/sup n/ protection architecture.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".