DR-PIFO: A Dynamic Ranking Packet Scheduler Using a Push-In-First-Out Queue
Bibliographic record
Abstract
Software-defined Networking (SDN) introduced the decoupling of control and data forwarding planes. Despite advances in the programmability of SDNs, there remains a strong need for a fully programmable packet scheduler in the data plane. In this context, the ability to adapt to various traffic patterns and the expressiveness of schedulers are of paramount importance. This paper introduces the Dynamic Ranking Push-In-First-Out (DR-PIFO), as an algorithmic model that can be used to develop programmable packet schedulers based on PIFO queues. The DR-PIFO is a highly expressive model, capable of expressing a wide range of work-conserving, non-work-conserving, and hierarchical scheduling algorithms. Additionally, its dynamic ranking capabilities allow for real-time updates to the packet’s priority within the scheduler. The proposed solution also performs error detection in the departure order of packets, which is essential to avoid starvation in strict priority scheduling. These features are crucial when implementing popular scheduling algorithms such as the pFabric. The DR-PIFO is evaluated through its algorithmic properties and by implementing two distinct case studies. Its performance is further evaluated by incorporating it as an external module, written in a high-level language, and integrating it with software switches implemented using the P4 language. The results illustrate the superior expressiveness of DR-PIFO over state-of-the-art models such as PIFO and PIEO and confirm that it is an algorithm-agnostic model. Thus, DR-PIFO represents a promising solution for implementing more fully programmable packet schedulers in SDNs, with the potential to improve performance and adaptability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".