Reducing the Impact of RTT Unfairness using P4-Programmable Data Planes
Bibliographic record
Abstract
This paper presents a system that mitigates the Round-trip Time (RTT) unfairness issue in non-programmable networks using P4-programmable data planes. In traditional loss-based congestion control algorithms (CCAs), RTT unfairness occurs when the flows with shorter RTTs obtain higher bandwidth shares with respect to the flows with longer RTTs. This behavior occurs due to the faster recovery period that flows with shorter RTTs experience after a loss event. On the other hand, more recent CCAs, such as the Bottleneck Bandwidth and Round-trip Time (BBR), present the opposite behavior, where the flows with longer RTTs achieve higher throughput than the ones with shorter RTTs. In this paper, the proposed system employs a P4-programmable data plane to monitor the RTT of flows traversing a non-programmable router at line rate using passive taps. The P4-programmable data plane analyzes the RTT of each flow, sub-sequently segregating them into different queues. This separation is aimed at minimizing the interaction between flows with varying RTTs. Results show that implementing flow separation improves the fairness of long flows, reduces the RTT of individual flows allocated in different queues, and improves the Flow Completion Times (FCTs) of short flows. P4, RTT unfairness, Transmission Control Protocol (TCP), Congestion Control Algorithm (CCA), Bottleneck Bandwidth and Round-trip Time (BBR).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".