Control-Plane Congestion in Optical-Burst-Switched Networks
Bibliographic record
Abstract
We examine optical burst switching (OBS) networks with out-of-band header transmission and electronic processing. We present the first detailed analysis of the potential effects of control-plane processing limitations on the overall throughput and latency performance of OBS networks. We present an accurate analytical model that explicitly accounts for the header queuing process in core nodes, and we provide a set of design guidelines for provisioning OBS networks such that the control-plane does not become the throughput bottleneck of the system. We also estimate the minimum end-to-end latency associated with offsets and burst assembly that is required to ensure proper OBS operation. We find that ultrafast header-processing speeds (<100 ns per header) are not required for efficient OBS operation. We show that provisioning a header-offset size that corresponds to a header-processing-queue length of 50 is sufficient for a wide range of practical OBS systems. For a fully meshed network topology, a total end-to-end control latency that is 10 times longer than the duration of a single header-processing duration is required for proper network operation. By contrast, more sparsely connected networks, such as those deployed in the long haul and metro today, may require an average end-to-end control latency that is hundreds of times as large as the header-processing duration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".