Improving TCP Fairness in Non-Programmable Networks Using P4-Programmable Data Planes
Bibliographic record
Abstract
Round-trip Time (RTT) unfairness arises in traditional TCP loss-based Congestion Control Algorithms (CCAs) such as CUBIC when flows with shorter RTTs are allocated more bandwidth than those with longer RTTs. Conversely, newer CCAs such as the Bottleneck Bandwidth and Round-trip Time (BBR) exhibit the opposite behavior, where flows with longer RTTs are allocated more bandwidth than those with shorter RTTs. This paper presents a system that reduces RTT unfairness by rebalancing the router's queue according to the RTT of TCP flows. The proposed system uses a P4-programmable Data Plane (PDP) as a measurement tool to process a copy of the traffic on the network, between non-programmable (traditional) routers. The PDP generates per-flow fine-grained measurements of the RTT and the throughput of TCP flows, and creates control rules which are applied to a non-programmable router. These rules distribute the TCP flows into separate queues, according to a classification algorithm that mitigates RTT unfairness. Results show that the system improves fairness among competing flows and reduces the Flow Completion Time (FCT) of long flows, regardless of the CCA. Moreover, the system is capable of measuring the throughput of individual flows and rebalancing the bandwidth allocated to each queue. This approach ensures that the available bandwidth is effectively used.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".