Availability-Aware Key Service With Dedicated Path Protection in Quantum Key Distribution Optical Networks
Bibliographic record
Abstract
Quantum key distribution (QKD)-based optical networks utilize quantum mechanics to secure communications by establishing secure, albeit expensive, quantum channels. A critical criterion for these QKD networks is to ensure network availability, which requires each service to adhere to a minimum unavailability constraint to maintain maximum network availability. This paper tackles the challenge of providing dedicated path protection for a select set of key services within QKD optical networks, adhering to specific unavailability constraints to ensure the gap between working and protection paths' unavailability remains within the allowable limit for each services. Considering the constraints of the limited timeslot resources and unavailability for the dedicated-path protection, we develop integer linear programming (ILP) models, to minimize the overall timeslot consumption in QKD optical networks. Additionally, we propose two novel algorithms for timeslot and availability management as minimum timeslot consumption (MTC) algorithm, and maximum availability (MA) algorithm to reduce the timeslot consumption. Simulations indicate that the ILP model based solution achieves the lowest timeslot consumption, however, it requires considerably more processing time compared to the proposed MTC and MA algorithms. Moreover, the MTC and MA algorithms outperform conventional dedicated-path protection (CDP) and fixed routing (FR) algorithms in reducing total timeslot consumption and improving average unavailability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".