Coordinated Sketch-Based Traffic Monitoring in Dynamic Networks
Bibliographic record
Abstract
As modern networks grow in scale and speed, sketch-based algorithms have become essential tools for accurate and low-overhead network monitoring. Existing solutions for sketch-based network monitoring assume that traffic rates in the network are fixed and known a priori. However, this assumption fails to reflect the real-world scenario of dynamic traffic rates, leading to degraded monitoring accuracy. In this paper, we introduce a novel approach for coordinated sketch placement on programmable network devices such as switches and SmartNICs without relying on the fixed traffic rate assumption. Instead, we consider scenarios with only statistical information about rates, such as their means and variances. To this end, we show that the problem can be formulated as a second-order optimization problem. Given the computational challenges of solving such optimization problems, we present an approximation technique to derive a linear formulation which can be solved efficiently even for large instances of the problem. Our evaluations using realistic workloads and network settings reveal that, by accounting for dynamic traffic rates, our approach can increase monitoring accuracy in the network by up to 2 x across different workloads compared to the existing solutions that do not explicitly consider these dynamics.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".