Long-lifetime capacity upgrades of ring networks for unpredictable traffic
Bibliographic record
Abstract
Traffic forecasting is an important means to facilitate network capacity planning by predicting necessary link upgrades and link additions, but it is generally unable to take into account events that are hard to predict, e.g., breaking news, denial-of-service attacks, and failures. These events, which may be typically short-lived but whose impact on the network traffic load is significant, together with the hard to predict traffic due to the server farm based architecture of the Internet will increase the amount of unpredictable traffic. The so-called network lifetime metric, which measures the ability of a network to sustain unexpected changes and shifts in traffic load, is becoming increasingly important. Very recently reported preliminary results show that the widely deployed bidirectional rings provide the smallest network lifetime, whereas chordal rings perform best in terms of network lifetime. In this paper, we first provide extensive numerical investigations of the network lifetime of bidirectional rings for realistic initial traffic scenarios. We then provide more detailed insights into the network lifetime of chordal rings and show that chordal rings not necessarily outperform bidirectional rings. Finally, we examine meshed rings and a novel hybrid ring-star network which clearly outperform not only bidirectional but also chordal rings in terms of network lifetime for a wide range of unexpected traffic changes and shifts
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".