Fault tolerance analysis of optical switching systems built on the vertical stacking of Banyan network
Bibliographic record
Abstract
Vertically stacked optical banyan (VSOB) networks are attractive for serving as optical switching systems due to the good properties of banyan network structures (such as the small depth and self-routing capability), and it, is expected that using the VSOB structure will lead to a better fault-tolerant capability because it is composed of multiple identical copies of banyan networks. Some analytical models have been developed to analyze the blocking behaviors of VSOB networks. However, none of theses analytical models has taken into account the fault-tolerant property of the VSOB networks. In this paper, we conduct the fault-tolerance analysis for the VSOB networks and present an analytical model for the blocking probability of VSOB networks when link faults are taken into account. We also conduct simulation to verify the model. Our analytical and simulation results show that our model can accurately describe the blocking behaviors of the VSOB networks at the presence of link failure. Our model also reveals a fact that by accepting a small link failure probability, the blocking behavior of a VSOB network is very similar to that of a fault-free one, which demonstrates our expectation of good fault-tolerant property of VSOB networks. The model is significant because it provides network developers a quantitative guidance to determine the effects of network failure on the overall blocking behaviors of VSOB networks and initiates a graceful compromise between blocking probability and hardware cost in a faulty VSOB network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".