Bibliographic record
Abstract
Increasingly, multimedia applications need higher bandwidth to provide better quality, for example in multi-party HD video conferencing. This demanding class of interactive applications simultaneously require high bandwidth and low end-to-end latency, a conflicting combination that is poorly supported in existing transports. Conventional wisdom dictates that network applications have a choice of transport protocols between TCP, if a reliable service model is desired, or UDP, if control over timing is required. In this paper we present Paceline, an enhanced transport we have devised to support interactive, high-bandwidth applications. Paceline enhances the transport service model to support application adaptation, through prioritization to provide timely delivery of important data, and cancellation to adapt the application rate to match available bandwidth. However, contrary to conventional wisdom, Paceline has not been implemented over UDP, nor does Paceline propose changes to TCP. We believe that the deployment obstacles and duplication of effort faced by solutions that alter or replace TCP entirely outweigh the challenges of mitigating its impairments. Instead, Paceline employs several mechanisms to support timely priority order delivery and cancellation above TCP: an application-level rate controller to reduce queueing delay due to excessive socket buffering, failover among connections to handle extreme cases of congestion, and message fragmentation to reduce the granularity of preemption. Our evaluation shows that Paceline improves upon conventional end-to-end latency shortcomings of using TCP, by factor of 3 in median latency and a factor of 4 in worst case latency. Meanwhile, Paceline is able to preserve TCP's performance in terms of fairness and utilization. Finally, we compare application performance with Paceline to a representative TCP alternative, Structured Stream Transport (SST), showing Paceline to be highly competitive.
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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 | 0.000 |
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 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".