A novel distributed destination routing-based Availability-Aware provisioning framework for differentiated protection services in optical mesh networks
Bibliographic record
Abstract
In optical mesh networks, different protection schemes can be used to satisfy the service availability against network failures. However, in order to satisfy a connectionpsilas service-availability requirement in a distributed controlled WDM with dynamic traffic and no wavelength converters, we need a framework to manage the provisioning process and to select a proper protection scheme. In this paper, we propose a novel distributed Availability-Aware provisioning framework based on intelligent destination routing to assign and manage the working and the protection paths as well as their wavelength(s) of each connection. Our proposed framework probes the k most reliable paths in parallel. The probing technique used in this framework is the first probing technique that probes each path as both a candidate working path and a candidate shared protection path at the same time. Moreover, we propose to use connection availability as a metric for providing differentiated protection services in WDM mesh networks. Based on the availability information collected, our provisioning strategy selects an appropriate level of protection to each connection. The effectiveness of our provisioning approaches is demonstrated through simulation results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".