Joint Optimization of TAS Scheduling and Synchronization Periodicity in Time Sensitive Networks
Bibliographic record
Abstract
Industry 4.0 applications require deterministic, low-latency and low-jitter communication. Ethernet, when augmented with Time Sensitive Networking standards like Time Aware Shaper (TAS), meets these requirements. For TAS to function accurately, precise network-wide time synchronization is required. However, due to clock drift of network devices, there can still be timing misalignment between devices, disrupting the TAS mechanism and compromising determinism. In our previous work, we proposed adjustment-based scheduling mechanisms that adjust TAS time slots to ensure deterministic latency and zero jitter. Despite their advantages, the approaches can be bandwidth inefficient compared to other forms of scheduling mechanisms. However, their efficiency can be improved by varying the synchronization periodicity.In this paper, we present a novel method to compute the optimal synchronization periodicity in a given network by balancing the synchronization overhead and the time-sensitive bandwidth requirement. This trade-off enables improvement of the overall network bandwidth efficiency without compromising determinism. Further it reveals the existence of an optimal operating region, a range of synchronization periodicity intervals where the network overhead cost remains low and stable. We validate the effectiveness of our approach through simulation-based case studies in realistic network scenarios. Compared to using the default synchronization periodicity value defined by IEEE standards, the proposed method reduces the overall network capacity overhead by up to 72% and 55% for the two scheduling method variants examined in this study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".