Implementing a high performance scheduling discipline WF2Q+ in FPGA
Bibliographic record
Abstract
Every server uses a scheduling discipline to decide the order in which the requests are to be served. A scheduling discipline should satisfy the following requirements: 1) it is easy to be implemented; 2) provides fairly distributed bandwidth to competing requests; 3) guarantees performance bounds for a wide range of traffic types; and 4) allows easy admission control decision. To date, a lot of scheduling disciplines have been proposed in the research literature, among which, the strict priority (SP), weighted fair queuing (WFQ) and weight round robin (WRR) are perhaps the three most widely adopted disciplines. However, the generalized processor sharing (GPS) discipline for packet scheduling best caters to the above properties. GPS uses an idealized fluid model that can't be precisely implemented in the real scenario. Worst case fair weighted fair queuing (WF2Q) is the closest packet approximation algorithm of the GPS discipline. WF2Q+ is an enhanced version of WF2Q and has a less time complexity. This paper first reviews the theory of WF2Q+, simplifies it for our implementation and then presents its detailed implementation in FPGA.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".