Dynamic FlexEthernet Defragmentation Under Time-Varying Traffic in Multi-layer Multi-domain Networks
Bibliographic record
Abstract
Traditional FlexEthernet (FlexE) defragmentation schemes have successfully been employed to reallocate the slots of affected FlexE clients during network changes such as FlexE physical link (PHY) failures in multi-layer multidomain (MLMD) networks in the context of fixed traffic rate. In such a context, constant slots of FlexE clients are statically pre-assigned using a round-robin algorithm before the network change takes place. However, in more realistic scenarios where traffic varies over time, this static assignment requires multiple defragmentation steps, potentially violating the maximum tolerated reconfiguration time and resulting in traffic loss. Therefore, a dynamic defragmentation scheme is required to efficiently move the affected slots without disrupting unaffected traffic. This paper introduces FDL, a semi-supervised learning approach designed to efficiently address the FlexE defragmentation problem under time-varying traffic conditions. FDL leverages an autoencoder for unsupervised pre-training, particularly due to the considerable amount of unlabeled data resulting from the unsolvable high-complexity optimization problem. To optimize throughput while adhering to reconfiguration time deadlines, FDL employs a gated recurrent unit (GRU) structure to forecast the future reassignment of FlexE clients’ slots over the defragmentation steps ahead. Simulation results demonstrate that the proposed FDL achieves a throughput that is 17.18% higher than a state-of-the-art approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".