Resource Allocation in Quantum-Key-Distribution Optical Data Center Networks
Bibliographic record
Abstract
The paper investigates quantum key resource allocation in quantum-key-distribution optical data center networks (QKD-ODCNs). A novel framework of priority queue with multiple security levels is first proposed to achieve a multi-class networking environment with adaptive security level classification. Accordingly, a novel integer linear programming (ILP) model is developed for the quantum key resource allocation (QKRA) process. To overcome the high computation complexity in solving the ILP, two heuristic approaches are developed, namely QKRA with adaptive strong security level (QKRA-ASSL) and QKRA with adaptive weak security level (QKRA-AWSL), respectively, aiming to achieve as close performance to that by the ILP model as possible in terms of success rate of connection request and network security level. The simulation results verify that the proposed QKRA-ASSL and QKRA-AWSL approaches not only maximize the network security score, but also improve the timeslot resource utilization without compromising the establishment success rate of connection request.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".