Differentiated Quality of Service in Survivable WDM Mesh Networks
Bibliographic record
Abstract
The emerging next generation optical transport WDM networks, with reconfigurable optical switches, offer a promising solution to the ever-increasing demand for high bandwidth and flexible connectivity. In order to meet the needs of such a demand, the trend in current backbone and access network development is moving toward a unified solution that will support different classes of service such as voice, data, and a large range of multimedia applications. However, those applications come with different qualities of service (i.e., bandwidth, reliability, and availability) depending on their requirements and on how much the users are willing to pay for the services. In the design of protection schemes in survivable WDM networks, there is a trade-off to be set between the capacity efficiency and the quality of service parameters. Differentiation of the provided quality of service can help in finding an appropriate trade-off between network cost and quality of service, for both service providers and customers. In this paper, we propose different network design optimization models in order to optimize two quality of service (QoS) protection parameters: Protection capacity sharing and recovery delay. We use shared protection schemes based on pre-configured structures that are pre-cross connected ahead of failures, and that are dynamically reconfigured in case of a failure. The resulting optimization models are solved using large scale optimization tools in order to ensure scalable solutions. Comparisons are conducted on different network and traffic instances, and a thorough analysis is made, exploring the added values of pre-cross connected protections structures on protection QoS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".