Dynamic lightpath restoration based on bidirectional initiation for wavelength-routed WDM networks
Bibliographic record
Abstract
Dynamic restoration is one of the basic survivability paradigms for surviving single-link failures in wavelength-routed wavelength division multiplexing (WDM) networks. In dynamic restoration, no spare network resources are reserved before a network failure occurs. The network must dynamically discover spare network resources to recover the disrupted network services after a network failure occurs. Accordingly, dynamic restoration is efficient in resource utilisation but slow in service recovery. Fast provisioning of service recovery is a great concern with dynamic restoration. A bidirectional initiation restoration mechanism (BIRM) is proposed for dynamic path restoration in wavelength-routed WDM networks. BIRM differs from existing path restoration mechanisms in that it allows both the source node and the destination node of a broken lightpath to respectively initiate a path restoration process in the event of a link failure. The purpose is to reduce the path restoration time so that a backup path can be established quickly for each broken lightpath that traverses a failed link. Based on BIRM, a dynamic path restoration protocol is also presented. Moreover, the performance of BIRM is evaluated through simulation experiments in terms of the path restoration time and request blocking probability.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".