MAMS: Mobility-Aware Multipath Scheduler for MPQUIC
Why this work is in the frame
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Bibliographic record
Abstract
Multi-homing technologies are promising to support seamless handoff and non-interrupted transmissions. Scheduling packets across multiple paths, however, has the known issue of out-of-order (OFO) due to the heterogeneity of the paths, which is detrimental to users’ quality of experience (QoE). Wireless link characteristics undergo a fast change over time in mobile environments, thus aggravating the OFO issue. In this paper, we present a novel mobility-aware multipath QUIC (MMQUIC) framework in which interactions between link and transport layers are introduced so that the scheduler at a mobile sender is aware of uplink variations, and a new ACK packet structure is designed to inform the scheduler of downlink variations when the receiver is mobile. Based on MMQUIC, a Mobility-Aware Multipath Scheduler (MAMS) is developed, which forecasts the path conditions in successive time slots based on historical and current end-to-end (E2E) path conditions, along with wireless uplink/downlink conditions, and pre-allocates packets on multiple paths accordingly. We conduct a series of experiments to evaluate the performance of MAMS using network simulator 3 (ns-3). Simulation results demonstrate that MAMS effectively leverages the information related to mobility, achieving substantial performance gains w.r.t. the goodput and packet delay distribution under different mobility patterns.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it