Bibliographic record
Abstract
Traffic grooming consists of packing low rate streams onto a high speed lightpath in order to effectively use the network resources. Under dynamic traffic, rerouting of ongoing connections has been envisioned as a means, to be used very wisely, to reduce the connection blocking rate and to optimize the network resources. In a context of traffic with QoS constraints, only the delay tolerant ongoing connections are rerouted. In this paper, we design three new heuristics, one relying on a mathematical ILP (Integer Linear Program) model and two low complexity ones to carefully reroute ongoing connection requests in order to accommodate the new incoming connection requests, while minimizing connection disturbance. While the ILP model allows the full exploration of a limit on the overall number of reroutings, both heuristics are designed as low complexity heuristics in order to limit the number of rerouting per establishment of a new incoming connection request. Comparative computational results show that the two low complexity heuristics provide much better results in terms of the best compromise between maximizing the throughput and minimizing the number of disturbances of the already established connection requests.
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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".