Analysis and design of edge-based controllers supporting absolute QoS for optical bursts
Bibliographic record
Abstract
Feature Issue on Photonics in SwitchingOptical burst switching (OBS) is a collision-based scheme in nature. It is a challenge to provide absolute quality of service (QoS), such as a guaranteed loss rate, for critical applications. Existing research often focuses on modifying the parameters or operations of the whole network, but there is hardly any work on controlling the loss rate based on operations at edge nodes only. We design an edge-based controlling scheme to achieve a guaranteed loss performance for an optical burst flow. Starting with the analysis of traffic characteristics of burst flows that are assembled by different algorithms, we investigate the relationship of major assembly parameters and the loss performances of different flows. Several loss predictors and assembly adjusters are designed in order to achieve a desired loss rate for a burst flow. Our control system does not affect the packet transmission load, which is especially useful for some critical applications such as multimedia streams. Our design can be applied to various networks using different assembly algorithms, signaling protocols, contention resolution methods, or varying network inputs. Compared with existing control schemes for OBS networks, our method is simple, efficient, and distributed for easy implementation using the information at edge nodes. The simulation results demonstrate that our system has an accurate and stable operation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".