Some analysis of call blocking probabilities in hierarchical WDM networks offering multiclass service
Bibliographic record
Abstract
Abstract We envision a large‐scale multiclass optical network in which lightpaths are dynamically set up and terminated between source and destination nodes and these lightpaths traverse through a number of interconnected WDM networks in different administrative domains. In this envisioned multiclass network, calls are classified by the number of wavelengths demanded per call and the statistics of call holding times (or service subscription periods). We present an approximation method for the performance analysis of such a large‐scale multiclass optical WDM network. We model the large‐scale optical network as a two‐level hierarchical multiclass loss network; the lower level of hierarchy consists of individual optical WDM networks (network segments). Each of these network segments is abstracted to a logical node at the higher level of hierarchy, and these logical nodes are connected to each other through logical links. We also assume that the call processing mechanism in this large‐scale optical network resorts to hierarchical routing. The algorithms presented in this paper computeper‐class end‐to‐end approximate blocking probabilities. Our experiments show that the simulation results match closely with the results of the proposed analytical approximation methods, thus validating the proposed methodology. Copyright © 2010 John Wiley & Sons, Ltd.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".