Towards Easy-to-Monitor Networks: Network Design and Measurement Path Construction
Bibliographic record
Abstract
In the era of Industry 4.0, networking has undergone a significant transformation driven by technologies like artificial intelligence. This shift necessitates the design of new network typologies to address the challenges posed by the exponential growth in network scale and complexity. Alongside this, network monitoring has gained increased importance due to the need for real-time operations. Consequently, there is a requirement for new network topologies that offer enhanced <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">monitorability</i>. To meet these evolving demands, this paper leverages a quantitative measure of network <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">monitorability</i> called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-identifiability, which forms the basis for two new network design problems: topology design and end-to-end measurement design. Both problems, however, are intractable. To tackle these problems, we conduct a systematic analysis of topological features that can simplify the design process. Based on this analysis, we propose an integrated algorithm that utilizes a novel dual-heuristic approach to generate well-formed topologies. Additionally, we develop an algorithm for creating measurement paths that accurately pinpoint failures. Through evaluation, we compare our dual-heuristic algorithm to theoretical optimal solutions in small-scale networks where brute-force search is feasible, demonstrating its near-optimality. We also showcase the effectiveness of our method in both topology and measurement design for large-scale networks.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".