Service differentiation in OFS network: Performance analysis
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 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).
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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