Fault-Resilient Topology Planning and Traffic Configuration for IEEE 802.1Qbv TSN Networks
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Bibliographic record
Abstract
Time-Sensitive Networking (TSN) is a set of IEEE standards that are being developed to enable a reliable and real-time communication based on Ethernet technology. It supports Time-Triggered (TT) traffic to allow a low latency as well as deterministic timing behavior. TSN adapts the concept of seamless redundancy to ensure interruption-free fault-resilience. In this paper, our goal is to synthesize a network topology that supports seamless redundant transmission for TT messages. Therefore, we propose a greedy heuristic algorithm for joint topology, routing, and schedule synthesis. The proposed algorithm is capable to generate fault-resilient topology that guarantee feasible routing and scheduling for TT traffic. In particular, the topology is constructed iteratively such that all messages are routed through disjoint paths with a feasible schedule and the network cost is minimized. To achieve this goal, we formulate the topology synthesis problem as iterative path selection problem. Starting from a weighted undirected graph which represents an initial fully-connected network, the cost implied of using each link is mapped as arcs weights in the graph. Then, we adapt Yen's algorithm to iteratively find the minimum-cost paths for the considered messages. The scalability and the efficiency of the proposed approach are demonstrated using 380 synthetic test cases. The results show that the proposed approach is capable of finding fault-resilient topology with up to 50% less cost compared to the typical approach. Moreover, the approach scalability is validated e.g., it handles 24 ECUs with 600 messages problems within an average time of 8 sec.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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