Availability-Guaranteed Distributed Provisioning Framework for Differentiated Protection Services in Optical Mesh Networks
Bibliographic record
Abstract
In transparent optical mesh networks, different protection schemes can be used to satisfy the service availability against network failures. However, in order to satisfy a connection's service availability requirement in distributed controlled optical networks with no global information, we need a framework to provision a working path with the appropriate level of protection for each connection request based on the requested level of availability. Moreover, we need a mechanism to guarantee the availability requirements of the existing connections in the network. In this paper, we propose a novel distributed provisioning framework to provide differentiated protection services in optical mesh networks, where nodes in such networks are Reconfigurable Optical Add/Drop Multiplexers (ROADMs). This framework examines the k most reliable paths as both candidate working paths and candidate shared protection paths at the same time, which gives the destination node the ability to apply an adaptive availability-guaranteed routing and wavelength assignment. Moreover, we propose two new distributed schemes to track the validity of the connections' availability requirements. Finally, we show the effectiveness of the proposed framework using extensive simulation experiments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".