GD-Aggregate: A WAN Virtual Topology Building Tool for Hard Real-Time and Embedded Applications
Bibliographic record
Abstract
The convergence of computer and physical world calls for next generation Wide Area Network (WAN) infrastructures for hard real-time and embedded applications. Such networks need virtual topologies to achieve scalability, configurability, and flexibility. Virtual topologies are made of virtual links, for which, the state-of-the-art building tool is Guaranteed Rate server based aggregates (GR- aggregates). However, common-practice weight assignment scheme couples GR-aggregate End-to-End (E2E) delay bound with aggregate's data throughput inverse proportionally. This is undesirable for many hard real-time embedded sensing/actuating applications, whose traffic has small data throughput but requires short E2E delay. We propose Guaranteed Delay server based aggregates (GD-aggregates), which allow assigning weights according to priorities instead of data throughput. This decouples E2E delay guarantee from data throughput, hence meets the needs of hard realtime embedded applications. In addition, GD-aggregates can be analyzed with simple closed form formulae, and can be easily planned with optimization tools.
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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 | 0.001 |
| 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) | 0.005 | 0.001 |
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".