Hirail: Core-Agnostic Deterministic Networks for Long-Distance Time-Sensitive IIoT Applications
Bibliographic record
Abstract
With the emergence of time-sensitive IIoT applications, such as remote operation and industrial control, a long-distance deterministic forwarding service is highly desirable. However, most of the existing research is limited to local area networks, or requires costly replacement of core network devices. Enabling incremental deterministic networks based on off-the-shelf technologies is a significant challenge. This paper designs a core-agnostic and cost-effective solution named Hirail to achieve the smooth evolution of long-distance deterministic networks. Firstly, we investigate that a time-discrete shaper (TDS) can be deployed at the ingress node to enable millisecond-level bounded delay. TDS functions similarly to the concept of buying time-stamped tickets for each flow prior to getting on a high-speed rail, thus avoiding the expensive modification of core devices. Then, to alleviate the flow aggregation problem under long-distance links, we utilize the inband network telemetry to construct the delay-aware network map and conduct adaptive source routing based on the map. Finally, an adjustable buffer at the last hop is devised for jitter reduction. Evaluation results show that Hirail can meet the bounded delay and jitter demands, and outperforms other solutions in terms of performance and overhead.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".