On Dynamic Server Provisioning in Multichannel P2P Live Streaming
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Bibliographic record
Abstract
To guarantee the streaming quality in live peer-to-peer (P2P) streaming channels, it is preferable to provision adequate levels of upload capacities at dedicated streaming servers, compensating for peer instability and time-varying peer upload bandwidth availability. Most commercial P2P streaming systems have resorted to the practice of overprovisioning a fixed amount of upload capacity on streaming servers. In this paper, we have performed a detailed analysis on 10 months of run-time traces from UUSee, a commercial P2P streaming system, and observed that available server capacities are not able to keep up with the increasing demand by hundreds of channels. We propose a novel online server capacity provisioning algorithm that proactively adjusts server capacities available to each of the concurrent channels, such that the supply of server bandwidth in each channel dynamically adapts to the forecasted demand, taking into account the number of peers, the streaming quality, and the channel priority. The algorithm is able to learn over time, has full Internet service provider (ISP) awareness to maximally constrain P2P traffic within ISP boundaries, and can provide differentiated streaming qualities to different channels by manipulating their priorities. To evaluate its effectiveness, our experiments are based on an implementation of the algorithm, which replays real-world traces.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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