Robust traffic grooming and infrastructure placement in OTN-over-DWDM networks
Bibliographic record
Abstract
The advent of next-generation networks has revolutionized modern networking practices through its improved service capability as well as its numerous emerging use cases. Coupled with the increasing number of connected devices, 5G and beyond (5G+) network traffic is expected to be increasingly diverse and high in volume. To address the large amount of data exchanged between the 5G+ core and external data networks, optical transport networks (OTNs) with dense wavelength-division multiplexing (DWDM) will be leveraged. In order to prepare for this increase in traffic, network operators (NOs) must develop and expand their existing backbone networks, requiring significant levels of capital expenditures. To this end, the traffic grooming and infrastructure placement problem is critical to supporting NO decisions. The work presented in this paper considers the traffic grooming and infrastructure placement problem for OTN-over-DWDM networks. The dynamicity and diversity of 5G+ network traffic are addressed through the use of robust optimization, allowing for increasing levels of solution conservativeness to protect against various levels of demand uncertainty. Furthermore, a robust traffic grooming and infrastructure placement heuristic (RGIP-H) solution capable of addressing the scalability concerns of the optimization problem formulation is presented. The results presented in this work demonstrate how the tuning of the robust parameters affects the cost of the objective function. Additionally, the ability of the robust solution to protect the solution under demand uncertainty is highlighted when the robust and deterministic solutions are compared during parameter deviation trials. Finally, the performance of the RGIP-H is compared to the optimization models when applied to larger network sizes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".