General Flow Characteristics of P2P Streaming Considering Impact to Network Load.
Bibliographic record
Abstract
This paper analyzes network traffic characteristics of peer-to-peer (P2P) video streaming services, which have been a recent source of annoyance for Internet service providers due to the large amount of data that they generate. We analyzed two popular P2P video streaming services, PPStream and PPLive, by capturing several hour-long packet streams using a personal computer. Through statistical analysis of the measured data, we identified flow-level characteristics of this P2P streaming. We observed that flow interarrival followed the Weibull distribution, and flow volume followed the Pareto distribution. Regarding network load, the interarrival among high-load flows followed an exponential distribution, and this distribution was valid as a general traffic model. Furthermore, flow volume almost followed a log-normal distribution, though the analysis failed to prove that this distribution can be used as a general model because the flow volume distribution greatly depends on P2P application.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| 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".