Quantitative QoS guarantees in labeled optical burst switching networks
Bibliographic record
Abstract
This paper presents a detailed architecture for providing quantitative QoS guarantees in labeled optical burst switching (LOBS) networks. Packets are assembled into data bursts based on their respective forwarding equivalence class (FEC) at ingress nodes. The burst assembly algorithm employs two parameters to control the burst blocking probability and burst assembly delay. We deploy a fair packet queueing (FPQ) algorithm in each edge node to regulate access to a wavelength scheduler. For LOBS core nodes, we present a novel approach that applies FPQ scheduling algorithms to the control plane of these nodes to guarantee fair bandwidth allocation. Based on the information provided by the queued control bursts, the core FPQ algorithm creates a virtual queue of data bursts in core nodes, then it selects the eligible control burst to be processed by the wavelength scheduler. In addition, we present analytical expressions for the worst case delay and the blocking probability in the proposed architecture. Simulation results demonstrate that the proposed architecture provides accurate and controllable service differentiation in LOBS networks.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".