LLDP Based Link Latency Monitoring in Software Defined Networks
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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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".