Understanding the Performance Gap Between Pull-Based Mesh Streaming Protocols and Fundamental Limits
Bibliographic record
Abstract
Pull-based mesh streaming protocols have recently received much research attention, with successful commercial systems showing their viability in the Internet. Despite the remarkable popularity in real-world systems, the fundamental properties and limitations of pull-based protocols are not yet well understood from a theoretical perspective, as there exists no prior work that studies the performance gap between the fundamental limits and the actual performance. In this paper, we develop a unified framework based on trellis graph techniques to mathematically analyze and understand the performance of pull-based mesh streaming protocols, with a particular focus on such a performance gap. We show that there exists a significant performance gap that separates the actual and optimal performance of pull-based mesh protocols. Moreover, periodic buffer map exchanges account for most of this performance gap. Our analytical characterization of the performance gap brings us not only a better understanding of several fundamental tradeoffs in pull-based mesh protocols, but also important insights on the design of practical streaming systems that can achieve high streaming rates and short initial buffering delays.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".