Novel Distributed Protocol for Dynamic Routing and Load Balancing for Optical Networks
Bibliographic record
Abstract
Over the last few years Dense Wavelength Division Multiplexing (DWDM) has emerged as one of the major transport technologies for the Internet infrastructure. It has also been seen that the Internet traffic has been growing at an exponential rate. In a DWDM network, an Optical cross-connects (OXCs) inter-connects two fibers. At an OXC, wavelengths can be added or dropped through the add/drop part of the OXC. In a DWDM network data is carried over a lightpath. A lightpath is a set of contiguous links that provide an end-to-end connection using a same (or different) wavelength(s). Wavelength conversion technology in optical domain is still in its infancy and is not commercially available yet. Currently, wavelength conversion is done electronically which is not very efficient in terms of processing speed. Therefore, wavelength conversion is generally avoided in optical networks or is used in a very limited way. Hence, in optical networks with no wavelength conversion, a lightpath is established using a single wavelength only to meet the Wavelength Continuity Constraint (WCC). Establishing a lightpath in a DWDM network involves two steps: computing a route and assigning a wavelength to the computed route, generally referred to as the Routing and Wavelength Assignment (RWA) Problem collectively. In the routing process, a shortest path is computed from the source to destination using some metric such as minimum hop count, link congestion etc). For wavelength assignment, a wavelength is searched that is available on each link of the computed route. The goal of the RWA is to maximize the number of connections. The RWA has been extensively studied in [1-3].
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".