Control-plane congestion and provisioning guidelines for OBS networks
Bibliographic record
Abstract
We present the first detailed analysis of the potential effects of electronic-control-plane throughput limitations on the overall loss and latency performance of OBS networks. We present an accurate analytical model for the header queuing process in core nodes taking into account the finite delay budget imposed by the header-offset size. We examine in detail the role of burst length and offset size on control-plane congestion and loss, and we provide a set of design guidelines for provisioning them such that the control-plane does not become the throughput bottleneck of the system. We find that ultra-fast header-processing speeds (< 100 ns per header) are not required for efficient OBS operation. We also show that provisioning a header-offset size that corresponds to a header-queue length of 50 is sufficient for realizing negligible control-plane loss and supporting burst lengths as small as megabits or hundreds of kilobits for OBS systems with up to 512 wavelengths.
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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".