Artificial Intelligence-Based Distributed Network Latency Measurement
Bibliographic record
Abstract
Network latency is an important metric for many networked systems. For small-scale systems, explicit measurements are carried out to collect N×(N-1) latency values to cover any pairs of nodes in the network. But this is not practical for large-scale systems due to the significant traffic and processing overhead needed for actual end-to-end latency measurements. Therefore, instead of actual measurements, researchers have proposed to estimate the round-trip times (RTT) to predict the latencies between all nodes within a network based on a small set of actual RTT measurements. However, such methods not only assume that the network is symmetric, which is not necessarily the case in reality, but also require time to converge. In this work, we present a novel method of network latency estimation using Artificial Intelligence (AI), specifically machine learning, which not only does not require any explicit measurements, but is also drastically faster than existing methods. Our method is trained using the well-known iConnect-Ubisoft dataset of actual RTT measurements, and uses the IP address as the primary input. Performance evaluations using two different datasets show that 73.6% and 59.3% of the measurements, respectively for each dataset, are within 20% estimation error.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".