A Study on the Design of Survivable Optical Virtual Private Networks (O-VPN)
Bibliographic record
Abstract
This paper tackles the resource allocation problem in wavelength division multiplexing (WDM) networks supporting virtual private networks (O-VPN), in which working, and spare capacity are allocated in the networks for satisfying a series of traffic matrices corresponding to a group of O-VPN. Based on the (M:N)nprotection architecture where multiple protection groups (PG) 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 quality of service (QoS) requirements in each O-VPN. ILP-I considers all the connection requests of each O-VPN in a single formulation, which may suffer from long computation time when the number of connection requests in an O-VPN is large. To trade capacity efficiency with computation complexity, ILP-II is developed such that each O-VPN can be further divided into multiple small PG based on specific grouping policies that satisfy multiple QoS requirements. With ILP-II, it is expected that all the working, and spare capacity of the O-VPN can be allocated with a polynomial time complexity provided that the size of each PG is well constrained. Experimental 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 more computation time. Although ILP-II is outperformed by ILP-I, it can handle the situation with an arbitrary size of O-VPN. We conclude that the proposed ILP-II model yields a scalable solution for the capacity planning in the survivable optical networks supporting O-VPN based on the (M:N)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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".