A Multipath Extension to the QUIC Module for ns-3
Bibliographic record
Abstract
Network transmission with multiple interfaces is desirable for the next-generation Internet to improve end-to-end performance and reliability. Multipath QUIC is thus proposed to utilize multiple interfaces for Internet transmission with QUIC, while QUIC is already standardized and active in use by several mainstream web browsers. However, the majority of the (MP)QUIC experimental platforms are built upon real systems or network emulators, which makes it challenging to investigate and experiment for further exploration. An MPQUIC simulation platform is still largely missing in the research community. In this paper, we present our implementation and improvement of MPQUIC based on the QUIC module for ns-3, along with a description of the features that we have implemented. We also demonstrate the performance of MPQUIC using an expanding series of experiments under various scenarios. Our implementation meets the demands for scalable multiple paths, flexible path schedulers, and compatible congestion control algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".