Bibliographic record
Abstract
Switching is one of the key functionalities in next generation optical networks. It might be performed by either an optical switch (optical-electrical-optical, or OEO) or a "purely" photonic switch (optical-optical-optical or OOO). Both switches are analyzed from two perspectives - as an individual network element, and as an integral part within the communication network. As an individual network element, the performance evaluation of the two switch types is based on the individual assessment of switch footprint and power dissipation, bandwidth utilization, scalability to high speed, transparency, interoperability, technology maturity and ability to manipulate data. Although both switch types have their own advantages as a network element, the full judgement of their role in next generation optical networks requires an overall network perspective. From that viewpoint, network functionalities such as grooming capabilities, scalability, traffic management, protection, line equalization and performance monitoring are those taken into account for comparative analyses to gain an understanding of the impacts of switch choice in the network. As a result of the comparative performance assessment, the merits and benefits of both switch types in actual network applications are analyzed and outlined. Although the paper evaluates some criteria for switch choice in a network, it points out potential technologies or techniques critical to next generation architectural solutions and protocols as well as the challenges to bridge the gap towards implementing flexible, cost-effective and dynamically provisioned networks of the future. Finally, the paper responds to one critical question - What is the expected role of each switch type in next generation applications and services?
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".