Bibliographic record
Abstract
An Enhanced-Internet network that provides ultra-low-latency guaranteed-rate (GR) communications for Cloud Services is proposed. The network supports two traffic classes, the Smooth and Best-Effort classes. Smooth traffic flows receive low-jitter GR service over virtual-circuit-switched (VCS) connections with negligible buffering and queueing delays, up to 100% link utilizations, deterministic end-to-end quality-of-service (QoS) guarantees, and improved energy efficiency. End-to-end delays are effectively reduced to the fiber “time of flight.” A new router scheduling problem called the Bounded Normalized-Jitter integer-programming problem is formulated. A fast polynomial-time approximate solution is presented, allowing TDM-based router schedules to be computed in microseconds. We establish that all admissible traffic demands in any packet-switched network can be simultaneously satisfied with GR-VCS connections, with minimal buffering. Each router can use two periodic TDM-based schedules to support GR-VCS connections, which are updated automatically when the router's traffic rate matrix changes. The design of a Silicon-Photonics all-optical packet switch with minimal buffering is presented. The Enhanced-Internet can: 1) reduce router buffer requirements by factors of ≥ 1000; 2) increase the Internet's aggregate capacity; 3) lower the Internet's capital and operating costs; and 4) lower greenhouse gas emissions through improved energy efficiency.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".