Understanding the Performance Gap Between Pull-Based Mesh Streaming Protocols and Fundamental Limits
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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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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 it