Survivable traffic grooming for scheduled demands
Bibliographic record
Abstract
There has been considerable research interest in the design of survivable grooming capable networks in recent years. For such networks, protection may take place at the lightpath level or at the connection level. The vast majority of the current work can be classified into one of two categories (i) static grooming, where the demands are allocated for the entire duration of the network and (ii) dynamic grooming, where the start times and durations of demands are generated randomly based on certain traffic distributions. In this paper, we propose a new technique for survivable traffic grooming under the scheduled traffic model that exploits knowledge of the connection holding times of traffic demands to lead to more efficient resource allocation. We present efficient integer linear program (ILP) formulations for the complete survivable traffic grooming problem in WDM networks. Our formulations can solve the joint problem of the topology design, traffic routing and RWA, using path protection at lightpath level. Our aim is to design a stable logical topology that can accommodate a collection of low-speed traffic demands with specified setup and teardown times. The objective function, considered in our ILP formulation, is to minimize the resource requirements. This can be easily modified to maximize the throughput under a given set of resources. We also have proposed a simplified version of our ILP formulations that can solve the problem in a way that is computationally more efficient. To the best of our knowledge, this is the first paper to address the survivable traffic grooming problem under the scheduled traffic model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".