Enhanced router bypass using fine granularity transport channels
Bibliographic record
Abstract
Internet traffic has been growing year-after-year for decades, but processing all that traffic through traditional IP routers has become an obstacle to further expansion. Router bypass has been introduced recently to overcome capacity limitations and processing costs of IP routers. With router bypass, a portion of traffic is provisioned to bypass the traditional router and is instead switched by the transport layer. Router bypass has been shown to potentially provide a significant savings in network costs, but these advantages are limited by a reduction in statistical multiplexing associated with the subdivision of available bandwidth into typically two distinct portions. This criticism has limited interest in bypass techniques. In parallel, G.709 Optical Transport Network (OTN) [1] technology with its recently introduced features such as direct support for packet (i.e., Generic Framing Procedure) and the Hitless Adjustment (HAO) have paved the way for a more dynamic and finer granularity transport layer. In this paper, we explore the impact of exploiting this finer granularity of provisioned bypass bandwidth and provisioning time on the efficacy of router bypass techniques. An OMNET++ simulation show that with finer bypassing granularity throughput can be enhanced up to 13% and packet drops can be reduced by up to 30%.
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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".