Diagnosing Network-wide P2P Live Streaming Inefficiencies,” http://cairo.csl.toronto.edu/papers/diagnose.pdf
Bibliographic record
Abstract
Abstract—Large-scale live peer-to-peer (P2P) streaming ap-plications have been successfully deployed in today’s Internet. While they can accommodate millions of users simultaneously with hundreds of channels of programming, there still commonly exist channels and times where and when the streaming quality is unsatisfactory. In this paper, based on more than two terabytes and one year worth of live traces from UUSee, a large-scale commercial P2P live streaming system, we show an in-depth network-wide diagnosis of streaming inefficiencies, commonly present in mesh-based P2P streaming systems. We first identify an evolutionary pattern of low streaming quality in the system and the distribution of streaming inefficiencies across various stream-ing channels. We then carry out an extensive investigation to explore the causes to such streaming inefficiencies over different times and across different channels at specific times. The original discoveries we have brought forward include the two-sided effects of peer population on the streaming quality in a channel, the significant impact of inter-peer bandwidth bottlenecks at peak times, and the inefficient utilization of server capacities across concurrent channels. We conclude with a number of suggestions to improve real-world large-scale P2P streaming. I.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".