Bibliographic record
Abstract
Providing network QoS involves, among other things, ensuring network survivability in spite of network faults. Fault recovery mechanisms should reduce recovery time, especially for real-time and mission-critical applications while guaranteeing QoS requirements, in terms of bandwidth and delay constraints and maximizing network resources utilization. In this paper, we propose a scalable recovery mechanism based on hierarchical networks. The proposed mechanism is based on inter-domain segmental restoration and is performed by a recovery module (RM) introduced for each domain of the hierarchy. The RM cooperates with path computation element (PCE) to perform recovery while maintaining QoS. Segmental restoration ensures faster recovery time by trying to recover failed paths as close as possible to where the fault occurred. The recovery mechanism aggregates fault notification messages to reduce the size of the signaling storm. In addition, the recovery mechanism ranks failed paths to reduce recovery time for high-priority traffic. We present simulation results conducted for different network sizes and hierarchy structures. Two metrics were considered: recovery time and signaling storm size. A significant decrease in the recovery time with increasing number of hierarchical levels for the same network size is observed. The larger the number of hierarchy levels, the smaller the number of network nodes in each domain and, generally, the faster the routing computations and routing tables search times. In addition, the recovery mechanism results in reducing recovery time for high priority traffic by nearly 90% over that of lower priority traffic. However, increasing the number of hierarchical levels results in a linear increase in signaling storm size.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".