Time-lined TCP for the TCP-friendly delivery of streaming media
Bibliographic record
Abstract
This paper introduces time-lined TCP (TLTCP). TLTCP is a protocol designed to provide TCP-friendly delivery of time-sensitive data to applications that are loss-tolerant, such as streaming media players. Previous work on unicast delivery, of streaming media over the Internet proposes using UDP and performs congestion control at the user level by regulating the application's sending rate. TLTCP, on the other hand is intended to be implemented at the transport level, and is based on TCP with modifications to support time-lines. Instead of treating all data as a byte stream TLTCP allows the application to associate data with deadlines. TLTCP sends data in a similar fashion to TCP with the deadline for a section of data has elapsed; at which point the now obsolete data is discarded in favor of new data. As a result, TLTCP supports TCP-friendly delivery of streaming media by retaining much of TCP's congestion control functionality. We describe an API for TLTCP that involves augmenting the recvmsg and sendmsg socket calls. We also describe how streaming media applications that use various encoding schemes like MPEG-1 can associate data with deadlines and use TLTCP's API. We use simulations to examine the behavior of TLTCP under a wide range of networks and workloads. We find that it indeed performs time-lined data delivery and under most circumstances the bandwidth is shared equally, among completing TLTCP and TCP flows. Moreover those scenarios under which TLTCP appears to be unfriendly are those under which TCP flows competing only with other TCP flows do not share bandwidth equitably.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".