Service differentiation in OFS network: Performance analysis
Bibliographic record
Abstract
This paper focuses on the design and analysis of QoS-based scheduling mechanism for service differentiation in Optical Flow Switching (OFS) networks. We consider two types of traffic flows: (i) delay-constraint flows (high priority flows) and (ii) Best-effort flows (low priority flows). In OFS, only optical cross connects (OXCs) are used for the switching function in the data plane and there is no buffering and processing involved at intermediate routes. Data flows through a simple all-optical data plane without any electronic processing except for the control plane. The flow QoS-based scheduling mechanism is to be implemented in the control plane using a priority queueing. We develop an analytical model to evaluate the performance of the considered OFS network using the proposed scheduling mechanism. Simulations are also conducted in order to validate the obtained analytical results. Numerical results have shown the efficiency of the QoS-based scheduling mechanism in satisfying delay constraint for DC flows especially when the contention for resource becomes noticeable (i.e. high network load).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".