Cross-layer throughput analysis for optical code labeled GMPLS networks
Bibliographic record
Abstract
The use of optical CDMA as a labeling mechanism in generalized multi-protocol label switching (GMPLS) optical networks significantly increases the traffic isolation capabilities. These networks, referred to as optical code labeled GMPLS (OC-GMPLS), have higher resource utilization due to the finer flow granularity introduced into the network. In this paper we present a cross-layer mathematical model for the throughput of OC-GMPLS networks, which provides a quantitative measure for the performance of optical networks throughput taking into consideration the effect of the physical layer. The proposed mathematical model incorporates the physical layer effects on the network layer performance by expressing the throughput as a function of the physical layer bit error rate, as well as the network traffic parameters such as the number of users and the packet length. Using the developed analytical model we were able to demonstrate the significant enhancement in the network performance due to the use of OC-GMPLS. We also used our analytical model to derive several optimum network operating points, which are of great importance to network designers and researchers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".