A novel method for flash crowd avoidance in P2P video on demand streaming via pre-release distribution
Bibliographic record
Abstract
In recent years, the high maintenance cost of centralized video on demand systems has led to the development of commercial peer to peer video on demand systems. These peer to peer systems help remove the cost and bandwidth limitations of a centralized group of servers. However, limited upload bandwidth in the current Internet combined with flash crowds limits the quality and smoothness of the streaming videos. In this paper, we present a novel idea to alleviate the problem of flash crowds, i.e. the sudden drastic increase in the number of requests for a video, such as at the video release time. The flash crowd problem causes potential network congestion that in turns increases the rate of video request rejections, thus reducing the video streaming capacity of the P2P systems. Our design supports video pre-release distribution by allowing users to subscribe to a movie series or a video from a schedule of upcoming videos. By using this information, encrypted versions of the video published by the publishers may be automatically retrieved even before the video is officially released. At the video release time, the decryption key of the encrypted video gets distributed to allow the user to decrypt the encrypted video and subsequently watch it. This pre-release video distribution system uses a hybrid peer to peer approach to handle distribution of schedules and decryption keys. We design a GUI and implement a prototype to demonstrate the feasibility and viability of this pre-release P2P distribution system.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".