Overlay Networks with Linear Capacity Constraints
Why this work is in the frame
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Bibliographic record
Abstract
Overlay networks are virtual networks residing overthe IP network, consequently, overlay links may share hiddenl ower-level bottlenecks. Previous work have assumed an independent overlay model: a graph with independent link capacities.We introduce a model of overlays which incorporates correlated link capacities and linear capacity constraints (LCC) to formulate hidden shared bottlenecks; we refer to these as LCC-overlays. We define metrics to qualitatively measure overlay quality in terms of its accuracy (in representing the true network topology) and efficiency (i.e., performance). Through analysis and simulations,we show that LCC-overlay is perfectly accurate and hence enjoys much higher efficiency than the inaccurate independent overlay. We discover that even a highly restricted LCC class — node basedLCC— yields near-optimal accuracy and significantly higher efficiency. We study two network flow problems in the context of LCC-graphs: Widest-Path and Maximum-Flow. Weprove that Widest-Path with LCC is NP-complete. We formulate Maximum-Flow with LCC as a linear program, and propose an efficient distributed algorithm to solve it. Based on the LCCmodel, we further study the problem of optimizing delay while still maintaining optimal or near-optimal bandwidth. We also outline a distributed algorithm to efficiently construct an overlay with node-based LCC.
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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 | 0.001 |
| 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 it