A Reinforcement Learning-Based Deflection Routing Scheme for Buffer-Less OBS Networks
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Bibliographic record
Abstract
Optical burst switching (OBS) is a promising switching paradigm for the next generation Internet. A buffer-less OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologically mature. However, this type of OBS networks suffers from relatively high loss probability caused by wavelength contentions at core nodes. This issue could prevent or, at least, delay the adoption of OBS networks as a solution for the next generation optical Internet. Deflection routing is one of the contention resolution approaches that have been proposed to tackle this problem. In addition to be cost-effective, it is also efficient in reducing loss probability, especially with low and moderate traffic loads. In this paper, we propose an adaptive reinforcement learning-based deflection routing scheme (RLDRS) which focuses on the route selection issue by choosing the optimal alternative output port in terms of both loss probability and delay when deflection is performed. Moreover, RLDRS limits the number of authorized deflections of each burst in order to reduce the additional traffic caused by deflection routing and to prohibit excessive deflections. Simulation results show that RLDRS reduces effectively loss probability and outperforms shortest path deflection routing (SPDR).
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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 it