A Study on the Design of Survivable Optical Virtual Private Networks (O-VPN)
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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) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> protection 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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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 | 0.001 |
| 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