Evaluation of a burst aggregation method in an optical burst switched agile all-photonic network
Bibliographic record
Abstract
This paper presents a burst aggregation method for an Agile All-Photonic Network (AAPN) operating under an asynchronous burst switched mode. The model combines both the timer-based and threshold-based approaches into a single composite burst assembly mechanism. This is evaluated semi-analytically for fixed length packets and Poisson arrivals and used as a special case to verify a more general OPNET Modeler simulation. The dependence of the blocking probability on different burst aggregation parameters is observed as well. The same procedure is extended to 'encapsulate' (aggregate) variable packet length traffic into 'envelopes' (bursts) matched to the time slots in an AAPN operating in a synchronous time-slotted mode. Results are presented for an emulation of this process using real IP network traffic from the local LAN using two encapsulation methods that differ depending upon whether 'envelope' boundaries are allowed to cross constituent packets or otherwise. Bandwidth utilization was measured for different encapsulation parameters and it is confirmed that the model with the boundaries allowed to cross packets (i.e., the model with packet segmentation) is more bandwidth-efficient even if the processing delay is slightly larger. The successful operation of the emulation system suggests as well that a simple, low-cost software implementation would be suitable to perform the burst/slot aggregation process in AAPN.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 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".