Quantitative QoS guarantees in labeled optical burst switching networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 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 it