Bibliographic record
Abstract
The evolution of network applications poses significant challenges to network service provisioning. Multipath routing and packet spraying techniques have become crucial in networks. TCP performance declines sharply on multipath setups where significant packet reordering occurs, as unordered transmissions are misinterpreted as packet loss and congestion signals. We propose the Multipath Routing Compatible (MPRC) congestion control, which utilizes the delay-sensitive Fast Retransmission Timeout (FastRTO) to decouple reordering from loss signals and enhance loss detection. This modification optimizes congestion window adjustments in multipath environments and handles packet reordering effectively, ensuring stable TCP throughput across multipath settings. Our algorithm was implemented on the NS-3 simulator platform and compared with other congestion control algorithms across various network topologies, in both single-path and multipath routing scenarios. The results demonstrate that MPRC can handle both sporadic and persistent packet reordering, ensuring steady throughput in multipath routing environments while maintaining compatibility and fairness in bandwidth competition, which paves the way for efficient congestion control adopting multi-path routing networks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".