Proxy stripping: a performance-enhancing technique for optical metropolitan area ring networks
Bibliographic record
Abstract
Metropolitan area ring networks can be categorized into metro edge and metro core rings. The traffic characteristics of metro edge and metro core rings are quite different. While metro edge rings exhibit a strongly hubbed traffic pattern (hot spots), traffic demands in metro core rings are much more uniform. We examine the throughput-delay performance of a buffer insertion ring with destination stripping and shortest path routing, which is the favored network type in the new high-performance standard for metropolitan area ring networks, IEEE 802.17 Resilient Packet Ring (RPR), and we investigate the ring's performance limitations under different traffic characteristics by means of analysis and simulation. Our probabilistic analysis considers arbitrary propagation delays, packet length distributions, and traffic matrices. In our numerical investigations we consider uniform, hot-spot, symmetric, and asymmetric traffic demands. Our findings show that the throughput-delay performance of buffer insertion rings deteriorates significantly under hot-spot traffic compared with uniform traffic. To mitigate this drawback, we propose and investigate the novel performance-enhancing proxy-stripping technique. Proxy stripping is used by a subset of ring nodes to send traffic across shortcuts of a dark-fiber star subnetwork. Our results show that proxy stripping dramatically improves the throughput-delay performance of buffer insertion rings not only under uniform traffic but also, in particular, under hot-spot traffic. Finally, we address the trade-offs of the proxy-stripping technique.
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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) | 0.000 | 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 | 0.001 | 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 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".