Advance lightpath provisioning in interdomain optical networks
Bibliographic record
Abstract
In interconnected optical networks, users submit lightpath requests at the time they wish to establish the lightpath. The service provider consults the information gathered by the interdomain routing protocols for available resources. For each request, the network must decide immediately whether to accept or reject the request. In this model, there is always the uncertainty of whether the user will be able to establish the desired lightpath at the desired time or not. Furthermore, in the context of a number of applications, e.g., grid applications, users need to set up lightpaths in advance to perform their activities that are planned in advance. We propose a new interdomain routing protocol called Advance Optical Routing Border Gateway Protocol (AORBGP) and a scheme that allows the setup of interdomain lightpaths in advance. AORBGP allows gathering information about interdomain paths and availability of wavelengths in the future. The proposed advance lightpath setup scheme makes use of AORBGP to get information about available resources (i.e., wavelengths) required to process lightpath setup requests. One of the key innovations of the scheme is that it provides the user with alternatives, carefully selected, when his or her request cannot be accommodated because of resource shortages. Indeed, the scheme provides the user with options to set up a lightpath later than the requested start time or with shorter duration than the requested duration. We performed a set of simulations to evaluate the benefits of the proposed scheme and the effect of a number of parameters on the performance of AORBGP.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".