Efficient Network Telemetry based on Traffic Awareness
Bibliographic record
Abstract
Network Telemetry (NT) is a crucial component in today’s networks, as it provides the network managers with important data about the status and behavior of the network elements. NT data are then utilized to get insights and rapidly take actions to improve the network performance or avoid its degradation. Intuitively, the more data are collected, the better for the network managers. However, the gathering and transportation of excessive NT data might produce an adverse effect, leading to a paradox: the data that are supposed to help actually damage the network performance. This is the motivation to introduce a novel NT framework that dynamically adjusts the rate in which the NT data should be transmitted. In this work, we present an NT scheme that is traffic-aware, meaning that the network elements collect and send NT data based on the type of traffic that they forward. The evaluation results of our Machine Learning-based mechanism show that it is possible to reduce by over 75% the network bandwidth overhead that a conventional NT scheme produces.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".