Delay Asymmetry Correction Model for Master-Slave Synchronization Protocols
Bibliographic record
Abstract
This paper proposes a Delay Asymmetry Correction (DAC) Model to enhance clock synchronization protocols based on a Master-Slave arrangement such as the IEEE 1588 PTP protocol. The purpose of this work is to mitigate the effects of unpredictable packet delay variations (PDV), which may aggravate asymmetric link delays on timing packets, in order to improve the synchronization accuracy of the slave clock with respect to the master clock. The key idea in our work is to filter clock updates derived from the master-slave message exchange, using only good samples for slave clock updates. The proposed solution is implemented in Network Simulator 2 (NS-2.34). NS-2 test cases are implemented according to the ITU-T G.8261 document covering various network loads and network conditions. Our simulation results indicate that the proposed solution improves the slave accuracy significantly, achieving almost perfect synchronization accuracy in the presence of a wide range of network traffic loads, network congestions, and temporary network outage. Furthermore, when there is a routing path change due to the failure in the network, the solution also maintains high slave clock accuracy with respect to the master clock.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".