LLDP based link latency monitoring in software defined networks
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Bibliographic record
Abstract
Current latency monitoring approaches for Software Defined Network often use the control plane as the infrastructure to inject time-stamp data packets as probe packets to measure the network latency at a particular time, but suffer from three major issues when the network latency needs to be continuously monitored: 1) the increased control plane's overhead, 2) the feasibility of using data packets as probe packets, and 3) the increasing measurement error using OpenFlow messages to measure the time from the controller to a switch as the network scale grows. To overcome these issues, this paper proposes link latency monitoring using time-stamped Link Layer Discovery Protocol (LLDP) packets, aided by a linear calibration function to reduce errors of measuring switch-controller delays. Time-stamping LLDP packets, which are used to discover the global network topology in SDNs, does not add extra workload to the control plane and the results always reach the controller thus while avoiding measurement failures that might occur in existing approaches. Our linear calibration function can reduce the measurement error to less than 5% of the link latency measured by ping in a network with up to 30 switches and the link latency not less than 1ms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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