A Hardware Architecture of a Dynamic Ranking Packet Scheduler for Programmable Network Devices
Bibliographic record
Abstract
The Dynamic Ranking Push-In-First-Out (DR-PIFO) is a novel programmable hardware queue architecture, introduced for the widely-used Portable Switch Architecture (PSA) used in modern network switches and routers. With the aid of a re-ranking mechanism, the DR-PIFO offers a flexible and expressive solution for a wide range of scheduling algorithms while still meeting line rate requirements. Our design, synthesized using TSMC’s 65nm technology, achieves the desired timing rate of 1GHz, while maintaining a throughput that matches the fastest existing schedulers while incurring a mere 15.5% increase in area compared to the state-of-the-art PIFO design. The proposed DR-PIFO’s hardware implementation is shown to closely approach the behavior and performance of its algorithmic model by efficiently executing various scheduling algorithms, leading to precise bandwidth distribution among traffic flows. Additionally, the DR-PIFO offers a significant reduction in the relative flow completion time (FCT) errors when implementing various scheduling policies with workloads collected from data centers. Thus, we believe that the DR-PIFO is a significant step toward making hardware packet schedulers more programmable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".