Enabling Rank-Based P4 Programmable Schedulers: Requirements, Implementation, and Evaluation on BMv2 Switches
Bibliographic record
Abstract
Software-defined networking (SDN) has revolutionized network infrastructure, offering programmability to meet evolving network demands. However, the fixed-function nature of the packet scheduler in current network equipment impedes the exploration of scheduling policies within a programmable network environment. This paper proposes a novel methodology to implement rank-based programmable schedulers in programmable BMv2 switches expressed with the network-specific programming language (P4). A proposed custom networking environment facilitates the study and evaluation of various scheduling policies. This environment is used to implement 20 different scheduling and shaping policies to identify the required language constructs and components needed to express these policies with the P4 language efficiently. Our experiments reveal that specific scheduling policies do not seamlessly align with a previously proposed architecture for rank-based scheduling policies. Thus, we propose rank-based versions for five previously reported scheduling policies, making them efficiently implementable in any rank-based schedulers and programmable network equipment. The reported results confirm that the rank-based versions of these scheduling policies accurately replicate the behavior and performance of the original policies, with a maximum error of 0.5% in the resulting flow completion times (FCTs).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".