A real-time approach for fast failure restoration in dynamic optical networks
Bibliographic record
Abstract
In this article, a real-time unreserved approach for fast failure restoration in dynamic optical networks is proposed. A common unreserved restoration technique allows for utilizing network capacity dynamically and with full flexibility. However, it usually takes long time as real-time redials are required during the restoration period. The restoration time is crucial to network performance because long restoration times can result in data loss that is not expected, especially in high-speed dynamic networks. The solution proposed in this article targets reducing the aggregated optical cross-connect switching time to minimize the failure restoration time by making two real-time events, signaling and physical OXC switching, work in parallel. With the proposed solution, the failure restoration time can be reduced to times that are similar to those achieved in the reserved schemes. Common unreserved schemes have an inherent need for retries since the first try after failure occurs cannot always be guaranteed due to lack of network resource. An enhanced implementation for the proposed solution is designed and described, which allows the features of the proposed solution to be fully realized and overcomes the inherent drawback of redundant OXC switching in common unreserved schemes. To evaluate the performance of the proposed solution, a simulation platform has been developed using the OPNET modeling and simulation tool. Through simulation experiments, it is shown that under a given network condition, the real-time path setup time for failure restoration with the proposed solution can be improved by /spl sim/68% compared to common unreserved schemes.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".