DDR: A Deadline-Driven Routing Protocol for Delay Guaranteed Service
Bibliographic record
Abstract
Time-sensitive applications have become increasingly prevalent in modern networks, necessitating the development of Delay-Guaranteed Routing (DGR) solutions. Finding an optimal DGR solution remains a challenging task due to the NP-hard nature of the problem and the dynamic nature of network traffic. In this paper, we propose Deadline-Driven Routing (DDR), a distributed traffic-aware adaptive routing protocol that addresses the DGR problem. Inspired by online navigation techniques, DDR leverages real-time traffic conditions to optimize routing decisions and ensure on-time packet delivery. By combining network topology-based path generation with real-time traffic knowledge, each router can adjust packet forwarding directions to meet its heterogeneous latency requirements. Comprehensive simulations on real-world network topologies demonstrate that DDR can consistently provide delay-guaranteed service in different network topologies with varying traffic conditions. In addition, DDR ensures backward compatibility with legacy devices and existing routing protocols, making it a viable solution for supporting delay-guaranteed service.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".