Compressed network monitoring for ip and all-optical networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We address the problem of efficient end-to-end network monitoring of path metrics in communication networks. Our goal is to minimize the number of measurements or monitors required to maintain an acceptable estimation accuracy. We present a framework based on diffusion wavelets and nonlinear estimation. Our procedure involves the development of a diffusion wavelet basis that is adapted to the monitoring problem. This basis exploits spatial and temporal correlations in the measured phenomena to provide a compressible representation of the path metrics. The framework employs nonlinear estimation techniques using l1 minimization to generate estimates for the unmeasured paths. We describe heuristic approaches for the selection of the paths that should be monitored, or equivalently, where hardware monitors should be located. We demonstrate how our estimation framework can improve the efficiency of end-to-end delay estimation in IP networks and reduce the number of hardware monitors required to track bit-error rates in all-optical networks (networks with no electrical regenerators).
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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 | 0.000 |
| 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 it