A dynamic k-routing algorithm in wavelength-routed optical networks
Bibliographic record
Abstract
Routing in a wavelength-routed network can be static or dynamic. In static routing, routes for each source-destination pair in a network are predetermined, whereas in dynamic routing, a route is computed dynamically for a connection request as it arrives in the network. Dynamic routing incorporates current network state in path computation to ensure an optimal path selection. In this paper, we propose an adaptive routing algorithm, a dynamic k-routing algorithm that computes a least cost path for a connection request. The algorithm computes at most k paths before a connection request is blocked. This paper assumes no wavelength conversion in the network and that the link state information is available to each node in the network. We compare the blocking performance of the proposed adaptive routing algorithm with fixed shortest path routing, fixed alternate shortest path routing and dynamic routing (using the shortest path with available capacity) and show that this algorithm performs better in terms of blocking probability and network utilization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".